Tootfinder

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
Qiyue Li, Yingxin Liu, Hang Qi, Jieping Luo, Zhizhang Liu, Jingjin Wu
https://arxiv.org/abs/2509.13933

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which us…

SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Ratun Rahman, Atit Pokharel, Md Raihan Uddin, Dinh C. Nguyen
https://arxiv.org/abs/2508.12477 https:/…

SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Quantum federated learning (QFL) is an emerging field that has the potential to revolutionize computation by taking advantage of quantum physics concepts in a distributed machine learning (ML) environment. However, the majority of available quantum simulators are primarily built for general quantum circuit simulation and do not include integrated support for machine learning tasks such as training, evaluation, and iterative optimization. Furthermore, designing and assessing quantum learning alg…

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
Ozer Ozturk, Busra Buyuktanir, Gozde Karatas Baydogmus, Kazim Yildiz
https://arxiv.org/abs/2509.13987

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
Machine learning models used for distributed architectures consisting of servers and clients require large amounts of data to achieve high accuracy. Data obtained from clients are collected on a central server for model training. However, storing data on a central server raises concerns about security and privacy. To address this issue, a federated learning architecture has been proposed. In federated learning, each client trains a local model using its own data. The trained models are periodic…

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
Marcel Gregoriadis, Jingwei Kang, Johan Pouwelse
https://arxiv.org/abs/2508.12353 https://

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
The centralized collection of search interaction logs for training ranking models raises significant privacy concerns. Federated Online Learning to Rank (FOLTR) offers a privacy-preserving alternative by enabling collaborative model training without sharing raw user data. However, benchmarks in FOLTR are largely based on random partitioning of classical learning-to-rank datasets, simulated user clicks, and the assumption of synchronous client participation. This oversimplifies real-world dynami…

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Lianshuai Guo, Zhongzheng Yuan, Xunkai Li, Yinlin Zhu, Meixia Qu, Wenyu Wang
https://arxiv.org/abs/2508.11530

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predomi…

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Zihou Wu (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China), Yuecheng Li (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China), Tianchi Liao (School of Software Engineering, Sun Yat-sen University, Zhuhai, China), Jian Lou (School of Software Engineering, Sun Yat-sen University, Zhuhai, China), Chuan Chen (School of Computer Science and E…

ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
Federated learning (FL) faces a critical dilemma: existing protection mechanisms like differential privacy (DP) and homomorphic encryption (HE) enforce a rigid trade-off, forcing a choice between model utility and computational efficiency. This lack of flexibility hinders the practical implementation. To address this, we introduce ParaAegis, a parallel protection framework designed to give practitioners flexible control over the privacy-utility-efficiency balance. Our core innovation is a strat…

Deep Learning based Moving Target Defence for Federated Learning against Poisoning Attack in MEC Systems with a 6G Wireless Model
Somayeh Kianpisheh, Tarik Taleb, Jari Iinatti, JaeSeung Song
https://arxiv.org/abs/2509.10914

Deep Learning based Moving Target Defence for Federated Learning against Poisoning Attack in MEC Systems with a 6G Wireless Model
Collaboration opportunities for devices are facilitated with Federated Learning (FL). Edge computing facilitates aggregation at edge and reduces latency. To deal with model poisoning attacks, model-based outlier detection mechanisms may not operate efficiently with hetereogenous models or in recognition of complex attacks. This paper fosters the defense line against model poisoning attack by exploiting device-level traffic analysis to anticipate the reliability of participants. FL is empowered …

Generalizable Federated Learning using Client Adaptive Focal Modulation
Tajamul Ashraf, Iqra Altaf Gillani
https://arxiv.org/abs/2508.10840 https://arxiv.o…

Generalizable Federated Learning using Client Adaptive Focal Modulation
Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate personalized focal modulation layers per client, outperforming traditional methods in non-IID and cross-domain settings. In this extended version, we propose AdaptFED, where we deepen the investigation of focal modulation in generalizable FL by …

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Zhanting Zhou, Jinshan Lai, Fengchun Zhang, Zeqin Wu, Fengli Zhang
https://arxiv.org/abs/2509.13895

FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Non-IID data and partial participation induce client drift and inconsistent local optima in federated learning, causing unstable convergence and accuracy loss. We present FedSSG, a stochastic sampling-guided, history-aware drift alignment method. FedSSG maintains a per-client drift memory that accumulates local model differences as a lightweight sketch of historical gradients; crucially, it gates both the memory update and the local alignment term by a smooth function of the observed/expected p…

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Ahaan Dabholkar, Atul Sharma, Z. Berkay Celik, Saurabh Bagchi
https://arxiv.org/abs/2509.11451 https://…

MAUI: Reconstructing Private Client Data in Federated Transfer Learning
Recent works in federated learning (FL) have shown the utility of leveraging transfer learning for balancing the benefits of FL and centralized learning. In this setting, federated training happens after a stable point has been reached through conventional training. Global model weights are first centrally pretrained by the server on a public dataset following which only the last few linear layers (the classification head) of the model are finetuned across clients. In this scenario, existing da…

FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Shouxu Lin, Zimeng Pan, Yuhang Yao, Haeyoung Noh, Pei Zhang, Carlee Joe-Wong
https://arxiv.org/abs/2510.10380

FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing the models' accuracies and training times in the MMFL setting requires adapting to data and system heterogeneity across clients as in single-model FL; these challenges are amplified in the MMFL setting due to additional heterogeneity across models. Neither existing solutions nor naïve extensions of single-model FL fra…

Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting
Ahmad Massud Tota Khel, Aissa Ikhlef, Zhiguo Ding, Hongjian Sun
https://arxiv.org/abs/2509.10123

Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting
We consider analog over-the-air federated learning, where devices harvest energy from in-band and out-band radio frequency signals, with the former also causing co-channel interference (CCI). To mitigate the aggregation error, we propose an effective denoising policy that does not require channel state information (CSI). We also propose an adaptive scheduling algorithm that dynamically adjusts the number of local training epochs based on available energy, enhancing device participation and lear…

Robust Clustered Federated Learning for Heterogeneous High-dimensional Data
Changxin Yang, Zhongyi Zhu, Heng Lian
https://arxiv.org/abs/2510.10576 https://…

Robust Clustered Federated Learning for Heterogeneous High-dimensional Data
Federated learning has attracted significant attention as a privacy-preserving framework for training personalised models on multi-source heterogeneous data. However, most existing approaches are unable to handle scenarios where subgroup structures coexist alongside within-group heterogeneity. In this paper, we propose a federated learning algorithm that addresses general heterogeneity through adaptive clustering. Specifically, our method partitions tasks into subgroups to address substantial b…

Differentially private federated learning for localized control of infectious disease dynamics
Raouf Kerkouche, Henrik Zunker, Mario Fritz, Martin J. K\"uhn
https://arxiv.org/abs/2509.14024

Differentially private federated learning for localized control of infectious disease dynamics
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localiz…

KV-Auditor: Auditing Local Differential Privacy for Correlated Key-Value Estimation
Jingnan Xu, Leixia Wang, Xiaofeng Meng
https://arxiv.org/abs/2508.11495 https://

KV-Auditor: Auditing Local Differential Privacy for Correlated Key-Value Estimation
To protect privacy for data-collection-based services, local differential privacy (LDP) is widely adopted due to its rigorous theoretical bound on privacy loss. However, mistakes in complex theoretical analysis or subtle implementation errors may undermine its practical guarantee. To address this, auditing is crucial to confirm that LDP protocols truly protect user data. However, existing auditing methods, though, mainly target machine learning and federated learning tasks based on centralized …

Enhancing Privacy Preservation and Reducing Analysis Time with Federated Transfer Learning in Digital Twins-based Computed Tomography Scan Analysis
Avais Jan, Qasim Zia, Murray Patterson
https://arxiv.org/abs/2509.08018

Enhancing Privacy Preservation and Reducing Analysis Time with Federated Transfer Learning in Digital Twins-based Computed Tomography Scan Analysis
The application of Digital Twin (DT) technology and Federated Learning (FL) has great potential to change the field of biomedical image analysis, particularly for Computed Tomography (CT) scans. This paper presents Federated Transfer Learning (FTL) as a new Digital Twin-based CT scan analysis paradigm. FTL uses pre-trained models and knowledge transfer between peer nodes to solve problems such as data privacy, limited computing resources, and data heterogeneity. The proposed framework allows re…

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Ahsan Habib Tareq, Iftekhar Haider
https://arxiv.org/abs/2510.06259

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on hete…

Federated Edge Learning for Predictive Maintenance in 6G Small Cell Networks
Yusuf Emir Sezgin, Mehmet \"Ozdem, Tu\u{g}\c{c}e Bilen
https://arxiv.org/abs/2509.11421 https:/…

Federated Edge Learning for Predictive Maintenance in 6G Small Cell Networks
The rollout of 6G networks introduces unprecedented demands for autonomy, reliability, and scalability. However, the transmission of sensitive telemetry data to central servers raises concerns about privacy and bandwidth. To address this, we propose a federated edge learning framework for predictive maintenance in 6G small cell networks. The system adopts a Knowledge Defined Networking (KDN) architecture in Data, Knowledge, and Control Planes to support decentralized intelligence, telemetry-dri…

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
Liang Qu, Jianxin Li, Wei Yuan, Penghui Ruan, Yuhui Shi, Hongzhi Yin
https://arxiv.org/abs/2508.10401

Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data. By keeping data local and relying on a central server to coordinate training across distributed clients, FedRSs protect user privacy while collaboratively learning global models. However, most existing FedRS frameworks adopt fully random client selection strategy in each training round, overlooking the statistical heterogeneity of…

Personalized Federated Learning-Driven Beamforming Optimization for Integrated Sensing and Communication Systems
Zhou Ni, Sravan Reddy Chintareddy, Peiyuan Guan, Morteza Hashemi
https://arxiv.org/abs/2510.06709

Personalized Federated Learning-Driven Beamforming Optimization for Integrated Sensing and Communication Systems
In this paper, we propose an Expectation-Maximization-based (EM) Personalized Federated Learning (PFL) framework for multi-objective optimization (MOO) in Integrated Sensing and Communication (ISAC) systems. In contrast to standard federated learning (FL) methods that handle all clients uniformly, the proposed approach enables each base station (BS) to adaptively determine its aggregation weight with the EM algorithm. Specifically, an EM posterior is computed at each BS to quantify the relative…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive O...
Pelikan, Azam, Feldman, Silovsky, Talwar, Brinton, Likhomanenko

Zero-Shot Decentralized Federated Learning
Alessio Masano, Matteo Pennisi, Federica Proietto Salanitri, Concetto Spampinato, Giovanni Bellitto
https://arxiv.org/abs/2509.26462 h…

Zero-Shot Decentralized Federated Learning
CLIP has revolutionized zero-shot learning by enabling task generalization without fine-tuning. While prompting techniques like CoOp and CoCoOp enhance CLIP's adaptability, their effectiveness in Federated Learning (FL) remains an open challenge. Existing federated prompt learning approaches, such as FedCoOp and FedTPG, improve performance but face generalization issues, high communication costs, and reliance on a central server, limiting scalability and privacy. We propose Zero-shot Decentrali…

Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Wanli Ni, Hui Tian, Shuai Wang, Chengyang Li, Lei Sun, Zhaohui Yang
https://arxiv.org/abs/2510.05713

Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL fram…

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
Jane Carney, Kushal Upreti, Gaby G. Dagher, Tim Andersen
https://arxiv.org/abs/2508.10042 …

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framewor…

Communication-Learning Co-Design for Differentially Private Over-the-Air Federated Distillation
Zihao Hu (The Chinese University of Hong Kong), Jia Yan (The Hong Kong University of Science and Technology), Ying-Jun Angela Zhang (The Chinese University of Hong Kong)
https://arxiv.org/abs/2508.06557 …

Communication-Learning Co-Design for Differentially Private Over-the-Air Federated Distillation
The ever-growing learning model size nowadays challenges the communication efficiency and privacy preservation of the traditional federated learning (FL). In this paper, we propose a novel differentially private (DP) over-the-air federated distillation (FD) framework, where wireless devices (WDs) periodically share noise-perturbed model outputs with the parameter server by harnessing the superposition property of multi-access channels. Accordingly, over-the-air FD enables the shared responsibil…

Empowering AI-Native 6G Wireless Networks with Quantum Federated Learning
Shaba Shaon, Md Raihan Uddin, Dinh C. Nguyen, Seyyedali Hosseinalipour, Dusit Niyato, Octavia A. Dobre
https://arxiv.org/abs/2509.10559

Empowering AI-Native 6G Wireless Networks with Quantum Federated Learning
AI-native 6G networks are envisioned to tightly embed artificial intelligence (AI) into the wireless ecosystem, enabling real-time, personalized, and privacy-preserving intelligence at the edge. A foundational pillar of this vision is federated learning (FL), which allows distributed model training across devices without sharing raw data. However, implementing classical FL methods faces several bottlenecks in heterogeneous dynamic wireless networks, including limited device compute capacity, un…

RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio, Taoufik Said, Muhammad Shafique, Mohamed Bennai
https://arxiv.org/abs/2509.04914

RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Quantum Federated Learning (QFL) merges privacy-preserving federation with quantum computing gains, yet its resilience to adversarial noise is unknown. We first show that QFL is as fragile as centralized quantum learning. We propose Robust Quantum Federated Learning (RobQFL), embedding adversarial training directly into the federated loop. RobQFL exposes tunable axes: client coverage $γ$ (0-100\%), perturbation scheduling (fixed-$\varepsilon$ vs $\varepsilon$-mixes), and optimization (fine-tun…

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications
Yuvraj Dutta, Soumyajit Chatterjee, Sandip Chakraborty, Basabdatta Palit
https://arxiv.org/abs/2508.08479

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications
Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected data, limiting their applicability in heterogeneous mobile environments with non-IID data distributions. This paper presents the first comprehensive benchmarking of federated learning (FL) strategies for throughput prediction in realistic 5G edge scenarios. …

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
Xian Qin, Xue Yang, Xiaohu Tang
https://arxiv.org/abs/2509.11870 https://

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to privacy leakage from gradient updates and Byzantine attacks from malicious clients. Existing solutions face a critical trade-off among privacy preservation, Byzantine robustness, and computational efficiency. We propose a novel scheme that effectively balances these competing objectives by integrating homomorphic encr…

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Jan Fiszer, Dominika Ciupek, Maciej Malawski
https://arxiv.org/abs/2510.07126

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate da…

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Kevin Kuo, Chhavi Yadav, Virginia Smith
https://arxiv.org/abs/2510.12595 https://

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse …

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
Soumia Zohra El Mestari, Maciej Krzysztof Zuziak, Gabriele Lenzini
https://arxiv.org/abs/2509.11974 htt…

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
Federated Learning (FL) enables collaborative model training across decentralised clients while keeping local data private, making it a widely adopted privacy-enhancing technology (PET). Despite its privacy benefits, FL remains vulnerable to privacy attacks, including those targeting specific clients. In this paper, we study an underexplored threat where a dishonest orchestrator intentionally manipulates the aggregation process to induce targeted overfitting in the local models of specific clie…

Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain, Lukas Esterle, Alexandros Iosifidis, Ruben Pauwels
https://arxiv.org/abs/2509.06553

Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Objectives: Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI. We compared FL with centralized (CL) and local learning (LL) for tooth segmentation in panoramic radiographs across multiple data corruption scenarios. Methods: An Attention U-Net was trained on 2066 radiographs from six institutions across four settings: baseline (unaltered data); label manipulation (dilated/missing annotations); image-quality man…

APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
Kejia Fan, Jianheng Tang, Zhirui Yang, Feijiang Han, Jiaxu Li, Run He, Yajiang Huang, Anfeng Liu, Houbing Herbert Song, Yunhuai Liu, Huiping Zhuang
https://arxiv.org/abs/2508.10732

APFL: Analytic Personalized Federated Learning via Dual-Stream Least Squares
Personalized Federated Learning (PFL) has presented a significant challenge to deliver personalized models to individual clients through collaborative training. Existing PFL methods are often vulnerable to non-IID data, which severely hinders collective generalization and then compromises the subsequent personalization efforts. In this paper, to address this non-IID issue in PFL, we propose an Analytic Personalized Federated Learning (APFL) approach via dual-stream least squares. In our APFL, w…

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Sehar Zehra, Hassan Jamil Syed, Ummay Faseeha
https://arxiv.org/abs/2510.10126

FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network ev…

Federated Learning Over LoRa Networks: Simulator Design and Performance Evaluation
Anshika Singh, Siddhartha S. Borkotoky
https://arxiv.org/abs/2508.10574 https://

Federated Learning Over LoRa Networks: Simulator Design and Performance Evaluation
Federated learning (FL) over long-range (LoRa) low-power wide area networks faces unique challenges due to limited bandwidth, interference, and strict duty-cycle constraints. We develop a Python-based simulator that integrates and extends the Flower and LoRaSim frameworks to evaluate centralized FL over LoRa networks. The simulator employs a detailed link-level model for FL update transfer over LoRa channels, capturing LoRa's receiver sensitivity, interference characteristics, block-fading effe…

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
https://arxiv.org/abs/2508.10212

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Underground mining operations rely on distributed sensor networks to collect critical data daily, including mine temperature, toxic gas concentrations, and miner movements for hazard detection and operational decision-making. However, transmitting raw sensor data to a central server for training deep learning models introduces significant privacy risks, potentially exposing sensitive mine-specific information. Federated Learning (FL) offers a transformative solution by enabling collaborative mo…

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Shiwei Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Jianbin Lin, Wenliang Zhong
https://arxiv.org/abs/2509.10161

FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative model training without sharing local data. Despite its advantages, FL suffers from substantial communication overhead, which can affect training efficiency. Recent efforts have mitigated this issue by quantizing model updates to reduce communication costs. However, most existing methods apply quantization only after local training, introducing quantization errors into the trained parameters a…

Taming Volatility: Stable and Private QUIC Classification with Federated Learning
Richard Jozsa, Karel Hynek, Adrian Pekar
https://arxiv.org/abs/2509.09997 https://

Taming Volatility: Stable and Private QUIC Classification with Federated Learning
Federated Learning (FL) is a promising approach for privacy-preserving network traffic analysis, but its practical deployment is challenged by the non-IID nature of real-world data. While prior work has addressed statistical heterogeneity, the impact of temporal traffic volatility-the natural daily ebb and flow of network activity-on model stability remains largely unexplored. This volatility can lead to inconsistent data availability at clients, destabilizing the entire training process. In th…

FedDAF: Federated Domain Adaptation Using Model Functional Distance
Mrinmay Sen, Ankita Das, Sidhant Nair, C Krishna Mohan
https://arxiv.org/abs/2509.11819 https://

FedDAF: Federated Domain Adaptation Using Model Functional Distance
Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy. FDA faces two primary challenges: domain shifts between source and target data and limited labeled data at the target. Most existing FDA methods focus on domain shifts, assuming ample target data, yet often neglect the combined challenges of both domain shifts and data scarcity. Moreover, approaches that …

Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Roopkatha Banerjee, Sampath Koti, Gyanendra Singh, Anirban Chakraborty, Gurunath Gurrala, Bhushan Jagyasi, Yogesh Simmhan
https://arxiv.org/abs/2508.08022

Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Real-time monitoring of power consumption in cities and micro-grids through the Internet of Things (IoT) can help forecast future demand and optimize grid operations. But moving all consumer-level usage data to the cloud for predictions and analysis at fine time scales can expose activity patterns. Federated Learning~(FL) is a privacy-sensitive collaborative DNN training approach that retains data on edge devices, trains the models on private data locally, and aggregates the local models in the…

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Rodrigo Tertulino
https://arxiv.org/abs/2508.10017 https…

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx
Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy. FL offers formal security guarantees when combined with Differential Privacy (DP). The integration of these technologies, however, introduces a significant trade-off between privacy and clinical utility, a challenge further complicated by the severe class imbalance often present in medical datasets. The research presented…

FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
Mohammad Hasan Narimani, Mostafa Tavassolipour
https://arxiv.org/abs/2509.10041

FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
Federated learning (FL) offers an innovative paradigm for collaborative model training across decentralized devices, such as smartphones, balancing enhanced predictive performance with the protection of user privacy in sensitive areas like Internet of Things (IoT) and medical data analysis. Despite its advantages, FL encounters significant challenges related to user privacy protection against potential attacks and the management of communication costs. This paper introduces a novel federated le…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
Anas Abouaomar, Mohammed El hanjri, Abdellatif Kobbane, Anis Laouiti, Khalid Nafil
https://arxiv.org/abs/2510.12727

Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top le…

FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Models
Matteo Caligiuri, Francesco Barbato, Donald Shenaj, Umberto Michieli, Pietro Zanuttigh
https://arxiv.org/abs/2508.03356

FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Models
Federated Learning (FL) is an established paradigm for training deep learning models on decentralized data. However, as the size of the models grows, conventional FL approaches often require significant computational resources on client devices, which may not be feasible. We introduce FedPromo, a novel framework that enables efficient adaptation of large-scale foundation models stored on a central server to new domains encountered only by remote clients. Instead of directly training the large m…

Privacy Enhancement in Over-the-Air Federated Learning via Adaptive Receive Scaling
Faeze Moradi Kalarde, Ben Liang, Min Dong, Yahia A. Eldemerdash Ahmed, Ho Ting Cheng
https://arxiv.org/abs/2510.03860

Privacy Enhancement in Over-the-Air Federated Learning via Adaptive Receive Scaling
In Federated Learning (FL) with over-the-air aggregation, the quality of the signal received at the server critically depends on the receive scaling factors. While a larger scaling factor can reduce the effective noise power and improve training performance, it also compromises the privacy of devices by reducing uncertainty. In this work, we aim to adaptively design the receive scaling factors across training rounds to balance the trade-off between training convergence and privacy in an FL syst…

Blockchain-Enabled Federated Learning
Murtaza Rangwala, Venugopal K R, Rajkumar Buyya
https://arxiv.org/abs/2508.06406 https://arxiv.org/pdf/2508.06406

Blockchain-Enabled Federated Learning
Blockchain-enabled federated learning (BCFL) addresses fundamental challenges of trust, privacy, and coordination in collaborative AI systems. This chapter provides comprehensive architectural analysis of BCFL systems through a systematic four-dimensional taxonomy examining coordination structures, consensus mechanisms, storage architectures, and trust models. We analyze design patterns from blockchain-verified centralized coordination to fully decentralized peer-to-peer networks, evaluating tr…

On-Device Multimodal Federated Learning for Efficient Jamming Detection
Ioannis Panitsas, Iason Ofeidis, Leandros Tassiulas
https://arxiv.org/abs/2508.09369 https://

On-Device Multimodal Federated Learning for Efficient Jamming Detection
Wireless networks face severe vulnerabilities from jamming attacks, which can significantly disrupt communication. Existing detection approaches are often unimodal, rely on centralized processing, and demand substantial computational resources, hindering scalability, efficiency, and deployment feasibility. To address these challenges, we introduce a multimodal Federated Learning (FL) framework for on-device jamming detection and classification that integrates spectrograms with cross-layer netwo…

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
Francisco Javier Esono Nkulu Andong, Qi Min
https://arxiv.org/abs/2509.10163

Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
As sixth-generation (6G) networks move toward ultra-dense, intelligent edge environments, efficient resource management under stringent privacy, mobility, and energy constraints becomes critical. This paper introduces a novel Federated Multi-Agent Reinforcement Learning (Fed-MARL) framework that incorporates cross-layer orchestration of both the MAC layer and application layer for energy-efficient, privacy-preserving, and real-time resource management across heterogeneous edge devices. Each age…

Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning
Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2509.10132

Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning
Bayesian Federated Learning (BFL) combines uncertainty modeling with decentralized training, enabling the development of personalized and reliable models under data heterogeneity and privacy constraints. Existing approaches typically rely on Markov Chain Monte Carlo (MCMC) sampling or variational inference, often incorporating personalization mechanisms to better adapt to local data distributions. In this work, we propose an information-geometric projection framework for personalization in para…

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
Shilong Wang, Jianchun Liu, Hongli Xu, Chenxia Tang, Qianpiao Ma, Liusheng Huang
https://arxiv.org/abs/2509.08409

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
Decentralized Federated Graph Learning (DFGL) overcomes potential bottlenecks of the parameter server in FGL by establishing a peer-to-peer (P2P) communication network among workers. However, while extensive cross-worker communication of graph node embeddings is crucial for DFGL training, it introduces substantial communication costs. Most existing works typically construct sparse network topologies or utilize graph neighbor sampling methods to alleviate the communication overhead in DFGL. Intu…

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
Yi Liu, Yang Liu, Leqian Zheng, Jue Hong, Junjie Shi, Qingyou Yang, Ye Wu, Cong Wang
https://arxiv.org/abs/2510.12494

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a.k.a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization…

Robust Segmented Analog Broadcast Design to Accelerate Wireless Federated Learning
Chong Zhang, Ben Liang, Min Dong, Ali Afana, Yahia Ahmed
https://arxiv.org/abs/2510.02701 http…

Robust Segmented Analog Broadcast Design to Accelerate Wireless Federated Learning
We consider downlink broadcast design for federated learning (FL) in a wireless network with imperfect channel state information (CSI). Aiming to reduce transmission latency, we propose a segmented analog broadcast (SegAB) scheme, where the parameter server, hosted by a multi-antenna base station, partitions the global model parameter vector into segments and transmits multiple parameters from these segments simultaneously over a common downlink channel. We formulate the SegAB transmission and …

FRIEREN: Federated Learning with Vision-Language Regularization for Segmentation
Ding-Ruei Shen
https://arxiv.org/abs/2510.02114 https://arxiv.org/pdf/2510…

FRIEREN: Federated Learning with Vision-Language Regularization for Segmentation
Federeated Learning (FL) offers a privacy-preserving solution for Semantic Segmentation (SS) tasks to adapt to new domains, but faces significant challenges from these domain shifts, particularly when client data is unlabeled. However, most existing FL methods unrealistically assume access to labeled data on remote clients or fail to leverage the power of modern Vision Foundation Models (VFMs). Here, we propose a novel and challenging task, FFREEDG, in which a model is pretrained on a server's …

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
Honghui Xu, Shiva Shrestha, Wei Chen, Zhiyuan Li, Zhipeng Cai
https://arxiv.org/abs/2509.09097

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
As on-device large language model (LLM) systems become increasingly prevalent, federated fine-tuning enables advanced language understanding and generation directly on edge devices; however, it also involves processing sensitive, user-specific data, raising significant privacy concerns within the federated learning framework. To address these challenges, we propose DP-FedLoRA, a privacy-enhanced federated fine-tuning framework that integrates LoRA-based adaptation with differential privacy in a…

Differentially Private Federated Quantum Learning via Quantum Noise
Atit Pokharel, Ratun Rahman, Shaba Shaon, Thomas Morris, Dinh C. Nguyen
https://arxiv.org/abs/2508.20310 http…

Differentially Private Federated Quantum Learning via Quantum Noise
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy. In the context of noisy intermediate-scale quantum (NISQ) devices, a key question arises: How can inherent quantum noise be leveraged to enforce differential privacy (DP) and protect model infor…

DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation
Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva, Sangarapillai Lambotharan
https://arxiv.org/abs/2509.08449

DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation
Federated Learning (FL) enables decentralized model training without sharing raw data, offering strong privacy guarantees. However, existing FL protocols struggle to defend against Byzantine participants, maintain model utility under non-independent and identically distributed (non-IID) data, and remain lightweight for edge devices. Prior work either assumes trusted hardware, uses expensive cryptographic tools, or fails to address privacy and robustness simultaneously. We propose DSFL, a Dual-S…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/7]:
- Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex ...
Diying Yang, Yingwei Hou, Weigang Wu

Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio, Giovanni Neglia
https://arxiv.org/abs/2509.08980

Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Training large-scale machine learning models incurs substantial carbon emissions. Federated Learning (FL), by distributing computation across geographically dispersed clients, offers a natural framework to leverage regional and temporal variations in Carbon Intensity (CI). This paper investigates how to reduce emissions in FL through carbon-aware client selection and training scheduling. We first quantify the emission savings of a carbon-aware scheduling policy that leverages slack time -- perm…

ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Sena Ergisi, Luis Ma{\ss}ny, Rawad Bitar
https://arxiv.org/abs/2509.09534 https://

ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Federated Learning (FL) emerged as a widely studied paradigm for distributed learning. Despite its many advantages, FL remains vulnerable to adversarial attacks, especially under data heterogeneity. We propose a new Byzantine-robust FL algorithm called ProDiGy. The key novelty lies in evaluating the client gradients using a joint dual scoring system based on the gradients' proximity and dissimilarity. We demonstrate through extensive numerical experiments that ProDiGy outperforms existing defen…

SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning
Borui Li, Li Yan, Jianmin Liu
https://arxiv.org/abs/2508.04265 https://

SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning
Federated Learning (FL) enables collaborative model training on decentralized data but remains vulnerable to gradient leakage attacks that can reconstruct sensitive user information. Existing defense mechanisms, such as differential privacy (DP) and homomorphic encryption (HE), often introduce a trade-off between privacy, model utility, and system overhead, a challenge that is exacerbated in heterogeneous environments with non-IID data and varying client capabilities. To address these limitatio…

Strategic Incentivization for Locally Differentially Private Federated Learning
Yashwant Krishna Pagoti, Arunesh Sinha, Shamik Sural
https://arxiv.org/abs/2508.07138 https://

Strategic Incentivization for Locally Differentially Private Federated Learning
In Federated Learning (FL), multiple clients jointly train a machine learning model by sharing gradient information, instead of raw data, with a server over multiple rounds. To address the possibility of information leakage in spite of sharing only the gradients, Local Differential Privacy (LDP) is often used. In LDP, clients add a selective amount of noise to the gradients before sending the same to the server. Although such noise addition protects the privacy of clients, it leads to a degrada…

Developing a Transferable Federated Network Intrusion Detection System
Abu Shafin Mohammad Mahdee Jameel, Shreya Ghosh, Aly El Gamal
https://arxiv.org/abs/2508.09060 https://

Developing a Transferable Federated Network Intrusion Detection System
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown attacks using knowledge from known attacks. To this end, we develop algorithms to maximize the number of transferability relationships. We propose a Convolutional Neural Network (CNN) model, along wi…

Dynamic Adaptive Federated Learning for mmWave Sector Selection
Lucas Pacheco, Torsten Braun, Kaushik Chowdhury, Denis Ros\'ario, Batool Salehi, Eduardo Cerqueira
https://arxiv.org/abs/2510.04183

Dynamic Adaptive Federated Learning for mmWave Sector Selection
Beamforming techniques use massive antenna arrays to formulate narrow Line-of-Sight signal sectors to address the increased signal attenuation in millimeter Wave (mmWave). However, traditional sector selection schemes involve extensive searches for the highest signal-strength sector, introducing extra latency and communication overhead. This paper introduces a dynamic layer-wise and clustering-based federated learning (FL) algorithm for beam sector selection in autonomous vehicle networks calle…

FedAPTA: Federated Multi-task Learning in Computing Power Networks with Adaptive Layer-wise Pruning and Task-aware Aggregation
Yachao Yuan, Zhen Yu, Jin Wang, Zhipeng Cheng, Jianhua Hu
https://arxiv.org/abs/2508.02230

FedAPTA: Federated Multi-task Learning in Computing Power Networks with Adaptive Layer-wise Pruning and Task-aware Aggregation
Federated Learning (FL) has shown considerable promise in Computing Power Networks (CPNs) for privacy protection, efficient data utilization, and dynamic collaboration. Although it offers practical benefits, applying FL in CPNs continues to encounter a major obstacle, i.e., multi-task deployment. However, existing work mainly focuses on mitigating FL's computation and communication overhead of a single task while overlooking the computing resource wastage issue of heterogeneous devices across m…

Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
Shun Takagi, Satoshi Hasegawa
https://arxiv.org/abs/2509.08709 htt…

Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing
In cross-device private federated learning, differentially private follow-the-regularized-leader (DP-FTRL) has emerged as a promising privacy-preserving method. However, existing approaches assume a semi-honest server and have not addressed the challenge of securely removing this assumption. This is due to its statefulness, which becomes particularly problematic in practical settings where clients can drop out or be corrupted. While trusted execution environments (TEEs) might seem like an obvio…

FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
Haocheng Jiang, Hua Shen, Jixin Zhang, Willy Susilo, Mingwu Zhang
https://arxiv.org/abs/2508.00636

FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
Federated learning is a distributed training framework vulnerable to Byzantine attacks, particularly when over 50% of clients are malicious or when datasets are highly non-independent and identically distributed (non-IID). Additionally, most existing defense mechanisms are designed for specific attack types (e.g., gradient similarity-based schemes can only defend against outlier model poisoning), limiting their effectiveness. In response, we propose FedGuard, a novel federated learning mechanis…

Fault-Tolerant Decentralized Distributed Asynchronous Federated Learning with Adaptive Termination Detection
Phani Sahasra Akkinepally, Manaswini Piduguralla, Sushant Joshi, Sathya Peri, Sandeep Kulkarni
https://arxiv.org/abs/2509.02186

Fault-Tolerant Decentralized Distributed Asynchronous Federated Learning with Adaptive Termination Detection
Federated Learning (FL) facilitates collaborative model training across distributed clients while ensuring data privacy. Traditionally, FL relies on a centralized server to coordinate learning, which creates bottlenecks and a single point of failure. Decentralized FL architectures eliminate the need for a central server and can operate in either synchronous or asynchronous modes. Synchronous FL requires all clients to compute updates and wait for one another before aggregation, guaranteeing con…

Perfectly-Private Analog Secure Aggregation in Federated Learning
Delio Jaramillo-Velez, Charul Rajput, Ragnar Freij-Hollanti, Camilla Hollanti, Alexandre Graell i Amat
https://arxiv.org/abs/2509.08683

Perfectly-Private Analog Secure Aggregation in Federated Learning
In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure multiparty computation has been proposed to enhance privacy. At the same time, perfect privacy can only be achieved by a uniform distribution of the masked local models to be aggregated. This raises a problem when working with real valued da…

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
Yuanchun Guo, Bingyan Liu, Yulong Sha, Zhensheng Xian
https://arxiv.org/abs/2509.08750

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
Federating heterogeneous models on edge devices with diverse resource constraints has been a notable trend in recent years. Compared to traditional federated learning (FL) that assumes an identical model architecture to cooperate, model-heterogeneous FL is more practical and flexible since the model can be customized to satisfy the deployment requirement. Unfortunately, no prior work ever dives into the existing model-heterogeneous FL algorithms under the practical edge device constraints and p…

Per-element Secure Aggregation against Data Reconstruction Attacks in Federated Learning
Takumi Suimon, Yuki Koizumi, Junji Takemasa, Toru Hasegawa
https://arxiv.org/abs/2508.04285

Per-element Secure Aggregation against Data Reconstruction Attacks in Federated Learning
Federated learning (FL) enables collaborative model training without sharing raw data, but individual model updates may still leak sensitive information. Secure aggregation (SecAgg) mitigates this risk by allowing the server to access only the sum of client updates, thereby concealing individual contributions. However, a significant vulnerability has recently attracted increasing attention: when model updates are sparse vectors, a non-zero value contributed by a single client at a given index c…

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Cosmin-Andrei Hatfaludi, Alex Serban
https://arxiv.org/abs/2509.05142 https://

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Federated learning has the potential to unlock siloed data and distributed resources by enabling collaborative model training without sharing private data. As more complex foundational models gain widespread use, the need to expand training resources and integrate privately owned data grows as well. In this article, we explore the intersection of federated learning and foundational models, aiming to identify, categorize, and characterize technical methods that integrate the two paradigms. As a …

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
Zainab Saad, Jialin Yang, Henry Leung, Steve Drew
https://arxiv.org/abs/2510.03970

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
The growing reliance on large-scale data centers to run resource-intensive workloads has significantly increased the global carbon footprint, underscoring the need for sustainable computing solutions. While container orchestration platforms like Kubernetes help optimize workload scheduling to reduce carbon emissions, existing methods often depend on centralized machine learning models that raise privacy concerns and struggle to generalize across diverse environments. In this paper, we propose a…

SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios
Borui Li, Li Yan, Junhao Han, Jianmin Liu, Lei Yu
https://arxiv.org/abs/2508.04100

SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios
Homomorphic Encryption (HE) prevails in securing Federated Learning (FL), but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in cross-device scenarios with heterogeneous data and system capabilities, traditional Selective HE methods deteriorate client straggling, and suffer from degraded HE overhead reduction performance. According…

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Jiaojiao Zhang, Yuqi Xu, Kun Yuan
https://arxiv.org/abs/2509.05213 https://arx…

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
This work addresses the key challenges of applying federated learning to large-scale deep neural networks, particularly the issue of client drift due to data heterogeneity across clients and the high costs of communication, computation, and memory. We propose FedSub, an efficient subspace algorithm for federated learning on heterogeneous data. Specifically, FedSub utilizes subspace projection to guarantee local updates of each client within low-dimensional subspaces, thereby reducing communicat…

LiFeChain: Lightweight Blockchain for Secure and Efficient Federated Lifelong Learning in IoT
Handi Chen, Jing Deng, Xiuzhe Wu, Zhihan Jiang, Xinchen Zhang, Xianhao Chen, Edith C. H. Ngai
https://arxiv.org/abs/2509.01434

LiFeChain: Lightweight Blockchain for Secure and Efficient Federated Lifelong Learning in IoT
The expansion of Internet of Things (IoT) devices constantly generates heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated and lifelong learning to overcome catastrophic forgetting. The extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks, and these risks may be obscured by performance degradation caused by spatial-temporal data heter…

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu
https://arxiv.org/abs/2509.07342

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a tempor…

Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT
Shengheng Liu, Ningning Fu, Zhonghao Zhang, Yongming Huang, Tony Q. S. Quek
https://arxiv.org/abs/2508.00341

Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT
The rising popularity of Internet of things (IoT) has spurred technological advancements in mobile internet and interconnected systems. While offering flexible connectivity and intelligent applications across various domains, IoT service providers must gather vast amounts of sensitive data from users, which nonetheless concomitantly raises concerns about privacy breaches. Federated learning (FL) has emerged as a promising decentralized training paradigm to tackle this challenge. This work focus…

DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
Mariona Jaramillo-Civill, Peng Wu, Pau Closas
https://arxiv.org/abs/2510.07132

DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a global model and fully personalized models. However, most CFL methods require the number of clusters K to be fixed a priori, which is impractical when the latent structure is unknown. We propose DPMM-CFL, a CFL algorithm that places a Dirichlet Process (DP) prior over the distribution of cluster parameters. This enables …

Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey
Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel
https://arxiv.org/abs/2509.21389

Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey
This survey explores the integration of Federated Learning (FL) with Network Intrusion Detection Systems (NIDS), with particular emphasis on deep learning and quantum machine learning approaches. FL enables collaborative model training across distributed devices while preserving data privacy-a critical requirement in network security contexts where sensitive traffic data cannot be centralized. Our comprehensive analysis systematically examines the full spectrum of FL architectures, deployment s…

FTTE: Federated Learning on Resource-Constrained Devices
Irene Tenison, Anna Murphy, Charles Beauville, Lalana Kagal
https://arxiv.org/abs/2510.03165 https://

FTTE: Federated Learning on Resource-Constrained Devices
Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy, but deployment on resource-constrained edge nodes remains challenging due to limited memory, energy, and communication bandwidth. Traditional synchronous and asynchronous FL approaches further suffer from straggler induced delays and slow convergence in heterogeneous, large scale networks. We present FTTE (Federated Tiny Training Engine),a novel semi-asynchronous FL framework t…

Stealth by Conformity: Evading Robust Aggregation through Adaptive Poisoning
Ryan McGaughey, Jesus Martinez del Rincon, Ihsen Alouani
https://arxiv.org/abs/2509.08746 https://…

Stealth by Conformity: Evading Robust Aggregation through Adaptive Poisoning
Federated Learning (FL) is a distributed learning paradigm designed to address privacy concerns. However, FL is vulnerable to poisoning attacks, where Byzantine clients compromise the integrity of the global model by submitting malicious updates. Robust aggregation methods have been widely adopted to mitigate such threats, relying on the core assumption that malicious updates are inherently out-of-distribution and can therefore be identified and excluded before aggregating client updates. In th…

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.07946

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
Yueqi Xie, Minghong Fang, Neil Zhenqiang Gong

Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
ZiHeng Huang, Di Wu, Jun Bai, Jiale Zhang, Sicong Cao, Ji Zhang, Yingjie Hu
https://arxiv.org/abs/2510.07022

Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
Machine unlearning is critical for enforcing data deletion rights like the "right to be forgotten." As a decentralized paradigm, Federated Learning (FL) also requires unlearning, but realistic implementations face two major challenges. First, fairness in Federated Unlearning (FU) is often overlooked. Exact unlearning methods typically force all clients into costly retraining, even those uninvolved. Approximate approaches, using gradient ascent or distillation, make coarse interventions that can…

Hypernetworks for Model-Heterogeneous Personalized Federated Learning
Chen Zhang, Husheng Li, Xiang Liu, Linshan Jiang, Danxin Wang
https://arxiv.org/abs/2507.22330 https://

Hypernetworks for Model-Heterogeneous Personalized Federated Learning
Recent advances in personalized federated learning have focused on addressing client model heterogeneity. However, most existing methods still require external data, rely on model decoupling, or adopt partial learning strategies, which can limit their practicality and scalability. In this paper, we revisit hypernetwork-based methods and leverage their strong generalization capabilities to design a simple yet effective framework for heterogeneous personalized federated learning. Specifically, we…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex ...
Diying Yang, Yingwei Hou, Weigang Wu

FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity
Ozgu Goksu, Nicolas Pugeault
https://arxiv.org/abs/2509.04107 https://arxi…

FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity
Federated Learning (FL) provides decentralised model training, which effectively tackles problems such as distributed data and privacy preservation. However, the generalisation of global models frequently faces challenges from data heterogeneity among clients. This challenge becomes even more pronounced when datasets are limited in size and class imbalance. To address data heterogeneity, we propose a novel method, \textit{FedQuad}, that explicitly optimises smaller intra-class variance and larg…

TAP: Two-Stage Adaptive Personalization of Multi-task and Multi-Modal Foundation Models in Federated Learning
Seohyun Lee, Wenzhi Fang, Dong-Jun Han, Seyyedali Hosseinalipour, Christopher G. Brinton
https://arxiv.org/abs/2509.26524

TAP: Two-Stage Adaptive Personalization of Multi-task and Multi-Modal Foundation Models in Federated Learning
Federated Learning (FL), despite demonstrating impressive capabilities in the training of multiple models in a decentralized manner, has been shown to produce a final model not necessarily well-suited to the needs of each client. While extensive work has been conducted on how to create tailored personalized models, called Personalized Federated Learning (PFL), less attention has been given to personalization via fine-tuning of foundation models with multi-task and multi-modal properties. Moreov…

Federated Computation of ROC and PR Curves
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04979 https://arxiv.org/pdf/2510.04979

Federated Computation of ROC and PR Curves
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trade-offs between true positive rate vs. false positive rate (ROC) or precision vs. recall (PR). However, in Federated Learning (FL) scenarios, where data is distributed across multiple clients, computing these curves is challenging due to privacy and communication constraints. Specifically, the server cannot access raw …

Adaptive Dual-Mode Distillation with Incentive Schemes for Scalable, Heterogeneous Federated Learning on Non-IID Data
Zahid Iqbal
https://arxiv.org/abs/2509.22507 https://

Adaptive Dual-Mode Distillation with Incentive Schemes for Scalable, Heterogeneous Federated Learning on Non-IID Data
Federated Learning (FL) has emerged as a promising decentralized learning (DL) approach that enables the use of distributed data without compromising user privacy. However, FL poses several key challenges. First, it is frequently assumed that every client can train the same machine learning models, however, not all clients are able to meet this assumption because of differences in their business needs and computational resources. Second, statistical heterogeneity (a.k.a. non-IID data) poses a m…

Federated Learning with Heterogeneous and Private Label Sets
Adam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson
https://arxiv.org/abs/2508.18774 https://

Federated Learning with Heterogeneous and Private Label Sets
Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assumed to be willing to share their entire label sets with other clients. Federated learning with private label sets, shared only with the central server, adds further constraints on learning algorithms and is, in general, a more difficult problem to solve. In this work, we study the effects of label set heterogeneity on…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/8]:
- Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
Archie Licudi, Anshul Thakur, Soheila Molaei, Danielle Belgrave, David Clifton

Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition
Kassahun Azezew, Minyechil Alehegn, Tsega Asresa, Bitew Mekuria, Tizazu Bayh, Ayenew Kassie, Amsalu Tesema, Animut Embiyale
https://arxiv.org/abs/2510.01113

Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition
Because biometric data is sensitive, centralized training poses a privacy risk, even though biometric recognition is essential for contemporary applications. Federated learning (FL), which permits decentralized training, provides a privacy-preserving substitute. Conventional FL, however, has trouble with interpretability and heterogeneous data (non-IID). In order to handle non-IID biometric data, this framework adds an attention mechanism at the central server that weights local model updates a…

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
Gwen Legate, Irina Rish, Eugene Belilovsky
https://arxiv.org/abs/2509.03503 https://

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
Federated learning enables collaborative model training across numerous edge devices without requiring participants to share data; however, memory and communication constraints on these edge devices may preclude their participation in training. We consider a setting in which a subset of edge devices are below a critical memory or communication threshold required to conduct model updates. Under typical federated optimization algorithms, these devices are excluded from training which renders thei…

FedCVD : Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt
https://arxiv.org/abs/2507.22963…

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communi…

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
Emmanouil Kritharakis, Antonios Makris, Dusan Jakovetic, Konstantinos Tserpes
https://arxiv.org/abs/2508.18060

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy by keeping local datasets on-device. In this work, we address FL settings where clients may behave adversarially, exhibiting Byzantine attacks, while the central server is trusted and equipped with a reference dataset. We propose FedGreed, a resilient aggregation strategy for federated learning that does not require any assumptions about the fraction of adversarial participants. Fe…

FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
Fahmida Islam, Adnan Mahmood, Noorain Mukhtiar, Kasun Eranda Wijethilake, Quan Z. Sheng
https://arxiv.org/abs/2509.20193

FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mitigating the communication overhead. Internet of Vehicles (IoV) is one of the promising applications, wherein FL can be utilized to train a model more efficiently. Since only a subset of the clients can participate in each FL training round, challenges arise pertinent to fairness in the client selection process. Over t…