Tootfinder

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…

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 …

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…

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…

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…

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…

I think he is trying to say he is an Avenger without realising he is the supervillain.
Also, does he know that while he is torrenting the world libraries into his bank account, you could set up for $10 a month your own Mastodon instance which is ten times better federated than his 'federated' Threads?

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…

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…

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…

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…

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 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…

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…

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.03248 https://

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Recent advancements in semantic communication have primarily focused on image transmission, where neural network (NN)-based joint source-channel coding (JSCC) modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between users and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we prop…

Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality)
Vivek Acharya
https://arxiv.org/abs/2510.03331 https://

Intelligent Healthcare Ecosystems: Optimizing the Iron Triangle of Healthcare (Access, Cost, Quality)
The United States spends nearly 17% of GDP on healthcare yet continues to face uneven access and outcomes. This well-known trade-off among cost, quality, and access - the "iron triangle" - motivates a system-level redesign. This paper proposes an Intelligent Healthcare Ecosystem (iHE): an integrated, data-driven framework that uses generative AI and large language models, federated learning, interoperability standards (FHIR, TEFCA), and digital twins to improve access and quality while lowering…

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 …

Scalable Asynchronous Federated Modeling for Spatial Data
Jianwei Shi, Sameh Abdulah, Ying Sun, Marc G. Genton
https://arxiv.org/abs/2510.01771 https://arx…

Scalable Asynchronous Federated Modeling for Spatial Data
Spatial data are central to applications such as environmental monitoring and urban planning, but are often distributed across devices where privacy and communication constraints limit direct sharing. Federated modeling offers a practical solution that preserves data privacy while enabling global modeling across distributed data sources. For instance, environmental sensor networks are privacy- and bandwidth-constrained, motivating federated spatial modeling that shares only privacy-preserving s…

Power Transform Revisited: Numerically Stable, and Federated
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04995 https://arxiv.org/pdf/2510.04995…

Power Transform Revisited: Numerically Stable, and Federated
Power transforms are popular parametric techniques for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to th…

Per witness, journalist Steve Held was detained by ICE.
He was filming an arrest when feds knocked a group of people backwards and grabbed him. Witness says it appeared targeted to her since he had been filming CBP interaction with the protester from start to finish.
https://bsky.app/profil…

unraveled (@unraveledpress.com)
Per witness, Steve was filming an arrest when feds knocked a group of people backwards and grabbed him. She says it appeared targeted to her since he had been filming CBP interaction with the protester from start to finish.

the other day I dreamt I saw this very 90s ad for the Fediverse. it was this “you wouldn’t steal a car” type screen with a very 90s font on it but it said “have you seen the fediverse?” and this gruff 90s voice read it out. then there were all these like abrupt cuts and closeups of various fedi stuff and then it ended with another one of those screens with the text on it that said “get federated.” and the voice read out the text again. anyways if anyone more talented wants to do that, feel f…

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…

FedFlex: Federated Learning for Diverse Netflix Recommendations
Sven Lankester, Manel Slokom, Gustavo de Carvalho Bertoli, Matias Vizcaino, Emmanuelle Beauxis Aussalet, Laura Hollink
https://arxiv.org/abs/2507.21115

FedFlex: Federated Learning for Diverse Netflix Recommendations
Federated learning is a decentralized approach that enables collaborative model training across multiple devices while preserving data privacy. It has shown significant potential in various domains, including healthcare and personalized recommendation systems. However, most existing work on federated recommendation systems has focused primarily on improving accuracy, with limited attention to fairness and diversity. In this paper, we introduce FedFlex, a federated recommender system for Netflix…

What’s Where?
In this blog, I mostly post things on which I have an opinion, or perhaps a discussion that I want to start or continue. I also have a blog at where I'll post more of the daily activity type of stuff, although I certainly do not post to it daily! I also have a micro.blog account at the contents of which get federated around very platforms, such as Mastadon, BlueSky and Threads. I'm currently building a photography website at So, have a wander around, and,…

What’s Where?
In this blog, I mostly post things on which I have an opinion, or perhaps a discussion that I want to start or continue. I also have a blog at where I’ll post more of the daily activity type …

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Ashkan Moradi, Fadila Zerka, Joeran S. Bosma, Mohammed R. S. Sunoqrot, Bendik S. Abrahamsen, Derya Yakar, Jeroen Geerdink, Henkjan Huisman, Tone Frost Bathen, Mattijs Elschot
https://arxiv.org/abs/2507.22790

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (csPCa) detection. Materials and Methods: A retrospective study was conducted using Flower FL to train a nnU-Net-based architecture for MRI prostate segmentation and csPCa detection, using data collected from January 2010 to August 2021. Model development included training and optimizing local epochs, federated rounds,…

Flow of Knowledge: Federated Fine-Tuning of LLMs in Healthcare under Non-IID Conditions
Zeyu Chen, Yun Ji, Bowen Wang, Liwen Shi, Zijie Zeng, Sheng Zhang
https://arxiv.org/abs/2510.00543

Flow of Knowledge: Federated Fine-Tuning of LLMs in Healthcare under Non-IID Conditions
Large language models (LLMs) show great promise in healthcare, but their applications are hindered by data privacy restrictions and the challenges of cross-institution collaboration. Sensitive medical data cannot be centralized, while non-independent and identically distributed (non-IID) characteristics across institutions further com- plicate convergence and fairness. To address these issues, we present a federated fine-tuning approach based on Low-Rank Adaptation (LoRA), enabling privacy-pres…

Feature Reconstruction Aided Federated Learning for Image Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.02048 https://ar…

Feature Reconstruction Aided Federated Learning for Image Semantic Communication
Research in semantic communication has garnered considerable attention, particularly in the area of image transmission, where joint source-channel coding (JSCC)-based neural network (NN) modules are frequently employed. However, these systems often experience performance degradation over time due to an outdated knowledge base, highlighting the need for periodic updates. To address this challenge in the context of training JSCC modules for image transmission, we propose a federated learning (FL)…

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…

Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids
Bochra Al Agha, Razane Tajeddine
https://arxiv.org/abs/2510.02371 https://

Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids
Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and operational behavior, creating a gateway to more severe targeted attacks. Detecting this threat is difficult because the signals it produces are faint, short-lived, and often disappear when traffic is examined by a single node or along a single timeline. This paper introduces a grap…

I’m an anarcho-syndicalist through and through, and I’m unapologetic about it. I reject all forms of authoritarianism, including Marxism-Leninism with its centralized, hierarchical party structure. Anarcho-syndicalism demands direct, horizontal worker control through federated assemblies with binding, recallable mandates as the only true path to emancipation.
If you intend to dispute this, I suggest thoroughly engaging with the relevant political theory first, because Marxism-Leninism,…

Adaptive Federated Distillation for Multi-Domain Non-IID Textual Data
Jiahao Xiao, Jiangming Liu
https://arxiv.org/abs/2508.20557 https://arxiv.org/pdf/250…

Adaptive Federated Distillation for Multi-Domain Non-IID Textual Data
The widespread success of pre-trained language models has established a new training paradigm, where a global PLM is fine-tuned using task-specific data from local clients. The local data are highly different from each other and can not capture the global distribution of the whole data in real world. To address the challenges of non-IID data in real environments, privacy-preserving federated distillation has been proposed and highly investigated. However, previous experimental non-IID scenarios…

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
Hongye Wang, Zhaoye Pan, Chang He, Jiaxiang Li, Bo Jiang
https://arxiv.org/abs/2507.22855 h…

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
Federated learning (FL) has emerged as a powerful paradigm for collaborative model training across distributed clients while preserving data privacy. However, existing FL algorithms predominantly focus on unconstrained optimization problems with exact gradient information, limiting its applicability in scenarios where only noisy function evaluations are accessible or where model parameters are constrained. To address these challenges, we propose a novel zeroth-order projection-based algorithm o…

Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification
Kaouther Mouheb, Marawan Elbatel, Janne Papma, Geert Jan Biessels, Jurgen Claassen, Huub Middelkoop, Barbara van Munster, Wiesje van der Flier, Inez Ramakers, Stefan Klein, Esther E. Bron
https://arxiv.org/abs/2508.21458

Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification
While foundation models (FMs) offer strong potential for AI-based dementia diagnosis, their integration into federated learning (FL) systems remains underexplored. In this benchmarking study, we systematically evaluate the impact of key design choices: classification head architecture, fine-tuning strategy, and aggregation method, on the performance and efficiency of federated FM tuning using brain MRI data. Using a large multi-cohort dataset, we find that the architecture of the classification…

I would love to see on @… pages a download to epub button, for offline reading some protocols.
https://www.w3.org/TR/activitypub/
I would love to see it here. (Not near a compu…

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…

Incentives in Federated Learning with Heterogeneous Agents
Ariel D. Procaccia, Han Shao, Itai Shapira
https://arxiv.org/abs/2509.21612 https://arxiv.org/pd…

Incentives in Federated Learning with Heterogeneous Agents
Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the contributor but boosts every participant. We introduce a game-theoretic framework that captures heterogeneous data: an agent's utility depends on who supplies each sample, not just how many. Agents aim to meet a PAC-style accuracy threshold at minimal personal cost. We show that uncoordinated play yields pathologies: pure…

Rates of Convergence of Generalised Variational Inference Posteriors under Prior Misspecification
Terje Mildner, Paris Giampouras, Theodoros Damoulas
https://arxiv.org/abs/2510.03109

Rates of Convergence of Generalised Variational Inference Posteriors under Prior Misspecification
We prove rates of convergence and robustness to prior misspecification within a Generalised Variational Inference (GVI) framework with bounded divergences. This addresses a significant open challenge for GVI and Federated GVI that employ a different divergence to the Kullback--Leibler under prior misspecification, operate within a subset of possible probability measures, and result in intractable posteriors. Our theoretical contributions cover severe prior misspecification while relying on our …

A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
Abhishek Sawaika, Swetang Krishna, Tushar Tomar, Durga Pritam Suggisetti, Aditi Lal, Tanmaya Shrivastav, Nouhaila Innan, Muhammad Shafique
https://arxiv.org/abs/2507.22908

Coward: Toward Practical Proactive Federated Backdoor Defense via Collision-based Watermark
Wenjie Li, Siying Gu, Yiming Li, Kangjie Chen, Zhili Chen, Tianwei Zhang, Shu-Tao Xia, Dacheng Tao
https://arxiv.org/abs/2508.02115

Coward: Toward Practical Proactive Federated Backdoor Defense via Collision-based Watermark
Backdoor detection is currently the mainstream defense against backdoor attacks in federated learning (FL), where malicious clients upload poisoned updates that compromise the global model and undermine the reliability of FL deployments. Existing backdoor detection techniques fall into two categories, including passive and proactive ones, depending on whether the server proactively modifies the global model. However, both have inherent limitations in practice: passive defenses are vulnerable to…

Replaced article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
Chengxi Li, Ming Xiao, Mikael Skoglund

FLEET: A Federated Learning Emulation and Evaluation Testbed for Holistic Research
Osama Abu Hamdan, Hao Che, Engin Arlsan, Md Arifuzzaman
https://arxiv.org/abs/2509.00621 https…

FLEET: A Federated Learning Emulation and Evaluation Testbed for Holistic Research
Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challe…

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…

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…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Jiaojiao Zhang, Yuqi Xu, Kun Yuan

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…

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 …

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…

Federated Consistency- and Complementarity-aware Consensus-enhanced Recommendation
Yunqi Mi, Boyang Yan, Guoshuai Zhao, Jialie Shen, Xueming Qian
https://arxiv.org/abs/2509.22659

Federated Consistency- and Complementarity-aware Consensus-enhanced Recommendation
Personalized federated recommendation system (FedRec) has gained significant attention for its ability to preserve privacy in delivering tailored recommendations. To alleviate the statistical heterogeneity challenges among clients and improve personalization, decoupling item embeddings into the server and client-specific views has become a promising way. Among them, the global item embedding table serves as a consensus representation that integrates and reflects the collective patterns across a…

Evaluating Selective Encryption Against Gradient Inversion Attacks
Jiajun Gu, Yuhang Yao, Shuaiqi Wang, Carlee Joe-Wong
https://arxiv.org/abs/2508.04155 https://

Evaluating Selective Encryption Against Gradient Inversion Attacks
Gradient inversion attacks pose significant privacy threats to distributed training frameworks such as federated learning, enabling malicious parties to reconstruct sensitive local training data from gradient communications between clients and an aggregation server during the aggregation process. While traditional encryption-based defenses, such as homomorphic encryption, offer strong privacy guarantees without compromising model utility, they often incur prohibitive computational overheads. To…

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…

Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Evan Chen, Seyyedali Hosseinalipour, Christopher G. Brinton, David J. Love
https://arxiv.org/abs/2509.01957

Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, their deployment in harsh or austere environments -- characterized by intermittent connectivity, limited computation, noisy data, and dynamically changing network topologies -- remains an open challenge. Existing distributed learning methods such as federated learning (FL) struggle to adapt in such settings due to their …

Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
https://arxiv.org/abs/2509.21800 https://arxiv.o…

Federated Learning of Quantile Inference under Local Differential Privacy
In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (SGD), whose local gradients are perturbed via a randomized mechanism with global parameters, making the procedure tolerant of communication and storage constraints without compromising statistical efficiency. Although the quantile loss and its corresponding gradient do not satisfy standard smoothness conditions typica…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[2/2]:
- FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

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…

Mix-modal Federated Learning for MRI Image Segmentation
Guyue Hu, Siyuan Song, Jingpeng Sun, Zhe Jin, Chenglong Li, Jin Tang
https://arxiv.org/abs/2509.02541 https://

Mix-modal Federated Learning for MRI Image Segmentation
Magnetic resonance imaging (MRI) image segmentation is crucial in diagnosing and treating many diseases, such as brain tumors. Existing MRI image segmentation methods mainly fall into a centralized multimodal paradigm, which is inapplicable in engineering non-centralized mix-modal medical scenarios. In this situation, each distributed client (hospital) processes multiple mixed MRI modalities, and the modality set and image data for each client are diverse, suffering from extensive client-wise m…

Commmunication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation
Le-Tuan Nguyen, Minh-Duong Nguyen, Seon-Geun Jeong, Dung D. Le, Quoc-Viet Pham
https://arxiv.org/abs/2509.26399

Commmunication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation
With the rapid emergence of foundation models and the increasing need for fine-tuning across distributed environments, Federated Low-Rank Adaptation (FedLoRA) has recently gained significant attention. Despite enormous potential, current FedLoRA methods face notable challenges due to inexact updates. Existing approaches have attempted to mitigate this issue, but they often introduce a \emph{local-global generalization gap} and incur \emph{substantial communication overhead}, limiting their scal…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou, Zhiqi Shen

SmartFLow: A Communication-Efficient SDN Framework for Cross-Silo Federated Learning
Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman
https://arxiv.org/abs/2509.00603 htt…

SmartFLow: A Communication-Efficient SDN Framework for Cross-Silo Federated Learning
Cross-silo Federated Learning (FL) enables multiple institutions to collaboratively train machine learning models while preserving data privacy. In such settings, clients repeatedly exchange model weights with a central server, making the overall training time highly sensitive to network performance. However, conventional routing methods often fail to prevent congestion, leading to increased communication latency and prolonged training. Software-Defined Networking (SDN), which provides centrali…

Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
Chunxu Zhang, Weipeng Zhang, Guodong Long, Zhiheng Xue, Riting Xia, Bo Yang
https://arxiv.org/abs/2509.19955

Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner. How to integrate multi-modality features into federated recommendation is still an open challenge in terms of efficiency, distribution heterogeneity, and fine-grained alignment. To address these challenges, we propose a novel multimodal fusion mechanism in federated recommendation settings (GFMFR). Specifically, it offloads multimodal representation learning to the serv…

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…

DySTop
Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao, Hongli Xu
https://arxiv.org/abs/2508.01996 https://arxiv.org/pdf/2508.01996

DySTop
Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under hetero…

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…

A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions
Yunqi Mi, Jiakui Shen, Guoshuai Zhao, Jialie Shen, Xueming Qian
https://arxiv.org/abs/2508.19620

A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions
Extending recommender systems to federated learning (FL) frameworks to protect the privacy of users or platforms while making recommendations has recently gained widespread attention in academia. This is due to the natural coupling of recommender systems and federated learning architectures: the data originates from distributed clients (mostly mobile devices held by users), which are highly related to privacy. In a centralized recommender system (CenRec), the central server collects clients' da…

Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.01780 https://arxiv.org/p…

Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP
Secure and interoperable integration of heterogeneous medical data remains a grand challenge in digital health. Current federated learning (FL) frameworks offer privacy-preserving model training but lack standardized mechanisms to orchestrate multi-modal data fusion across distributed and resource-constrained environments. This study introduces a novel framework that leverages the Model Context Protocol (MCP) as an interoperability layer for secure, cross-agent communication in multi-modal fede…

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez, Syed Ali Zaidi, Hamed Ahmadi
https://arxiv.org/abs/2507.21698

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication ef…

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…

Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead
Thanh Linh Nguyen, Marcela Tuler de Oliveira, An Braeken, Aaron Yi Ding, Quoc-Viet Pham
https://arxiv.org/abs/2510.00833

Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead
Federated unlearning (FUL) enables removing the data influence from the model trained across distributed clients, upholding the right to be forgotten as mandated by privacy regulations. FUL facilitates a value exchange where clients gain privacy-preserving control over their data contributions, while service providers leverage decentralized computing and data freshness. However, this entire proposition is undermined because clients have no reliable way to verify that their data influence has be…

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…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models
Maozhen Zhang, Mengnan Zhao, Bo Wang

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…

PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows
Renan Souza, Amal Gueroudji, Stephen DeWitt, Daniel Rosendo, Tirthankar Ghosal, Robert Ross, Prasanna Balaprakash, Rafael Ferreira da Silva
https://arxiv.org/abs/2508.02866

PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows
Foundation models, such as Large Language Models (LLMs), are increasingly used as core components of AI agents in complex, large-scale workflows across federated and heterogeneous environments. In agentic workflows, autonomous agents plan tasks, interact with humans and peers, and shape scientific outcomes. This makes transparency, traceability, reproducibility, and reliability essential. However, AI-based agents can hallucinate or reason incorrectly, and their decisions may propagate errors th…

Decentralized Federated Averaging via Random Walk
Changheng Wang, Zhiqing Wei, Lizhe Liu, Qiao Deng, Yingda Wu, Yangyang Niu, Yashan Pang, Zhiyong Feng
https://arxiv.org/abs/2508.21286

Decentralized Federated Averaging via Random Walk
Federated Learning (FL) is a communication-efficient distributed machine learning method that allows multiple devices to collaboratively train models without sharing raw data. FL can be categorized into centralized and decentralized paradigms. The centralized paradigm relies on a central server to aggregate local models, potentially resulting in single points of failure, communication bottlenecks, and exposure of model parameters. In contrast, the decentralized paradigm, which does not require …

FedBit: Accelerating Privacy-Preserving Federated Learning via Bit-Interleaved Packing and Cross-Layer Co-Design
Xiangchen Meng, Yangdi Lyu
https://arxiv.org/abs/2509.23091 http…

FedBit: Accelerating Privacy-Preserving Federated Learning via Bit-Interleaved Packing and Cross-Layer Co-Design
Federated learning (FL) with fully homomorphic encryption (FHE) effectively safeguards data privacy during model aggregation by encrypting local model updates before transmission, mitigating threats from untrusted servers or eavesdroppers in transmission. However, the computational burden and ciphertext expansion associated with homomorphic encryption can significantly increase resource and communication overhead. To address these challenges, we propose FedBit, a hardware/software co-designed f…

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…

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…

Graph Theory Meets Federated Learning over Satellite Constellations: Spanning Aggregations, Network Formation, and Performance Optimization
Fardis Nadimi, Payam Abdisarabshali, Jacob Chakareski, Nicholas Mastronarde, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.24932

Graph Theory Meets Federated Learning over Satellite Constellations: Spanning Aggregations, Network Formation, and Performance Optimization
We introduce Fed-Span, a novel federated/distributed learning framework designed for low Earth orbit satellite constellations. By leveraging graph-theoretic principles, Fed-Span addresses critical challenges inherent to distributed learning in dynamic satellite networks, including intermittent satellite connectivity, heterogeneous computational capabilities of satellites, and time-varying satellites' datasets. At its core, Fed-Span builds upon minimum spanning tree (MST) and minimum spanning fo…

Role-Aware Multi-modal federated learning system for detecting phishing webpages
Bo Wang, Imran Khan, Martin White, Natalia Beloff
https://arxiv.org/abs/2509.22369 https://

Role-Aware Multi-modal federated learning system for detecting phishing webpages
We present a federated, multi-modal phishing website detector that supports URL, HTML, and IMAGE inputs without binding clients to a fixed modality at inference: any client can invoke any modality head trained elsewhere. Methodologically, we propose role-aware bucket aggregation on top of FedProx, inspired by Mixture-of-Experts and FedMM. We drop learnable routing and use hard gating (selecting the IMAGE/HTML/URL expert by sample modality), enabling separate aggregation of modality-specific par…

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin
https://arxiv.org/abs/2507.22339

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Federated Learning (FL) presents a promising paradigm for enabling distributed intelligence in these resource-constrained and dynamic environments. However, achieving reliable convergence, while minimizing both processing time and energy consumption, remains a substantial challenge, particularly in heterogeneous and partia…

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…

OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks
Pranjal Naman, Yogesh Simmhan
https://arxiv.org/abs/2509.22922 https://

OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks
Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. However, in most real-world settings, such as financial transaction networks and healthcare networks, this data is localized to different data owners and cannot be aggregated due to privacy concerns. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model that iteratively aggregates loc…

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…

Tackling Federated Unlearning as a Parameter Estimation Problem
Antonio Balordi, Lorenzo Manini, Fabio Stella, Alessio Merlo
https://arxiv.org/abs/2508.19065 https://

Tackling Federated Unlearning as a Parameter Estimation Problem
Privacy regulations require the erasure of data from deep learning models. This is a significant challenge that is amplified in Federated Learning, where data remains on clients, making full retraining or coordinated updates often infeasible. This work introduces an efficient Federated Unlearning framework based on information theory, modeling leakage as a parameter estimation problem. Our method uses second-order Hessian information to identify and selectively reset only the parameters most se…

From Research to Reality: Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias {\AA}kesson, Johan \"Ostman, Salman Toor, Andreas Hellander
https://arxiv.org/abs/2508.19819

From Research to Reality: Feasibility of Gradient Inversion Attacks in Federated Learning
Gradient inversion attacks have garnered attention for their ability to compromise privacy in federated learning. However, many studies consider attacks with the model in inference mode, where training-time behaviors like dropout are disabled and batch normalization relies on fixed statistics. In this work, we systematically analyze how architecture and training behavior affect vulnerability, including the first in-depth study of inference-mode clients, which we show dramatically simplifies inv…

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Roberto Doriguzzi-Corin, Petr Sabel, Silvio Cretti, Silvio Ranise
https://arxiv.org/abs/2509.17836

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, which is limited in cybersecurity due to privacy and data-sharing restrictions. To address this challenge, Federated Learning (FL) has recently emerged as a novel paradigm that enables collaborative training of ML models across multiple clients while ensuring that sensitive data remains local. Nevertheless, Federated Av…

Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
Yebo Wu, Jingguang Li, Chunlin Tian, Zhijiang Guo, Li Li
https://arxiv.org/abs/2508.17209 https…

Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
Federated fine-tuning enables privacy-preserving Large Language Model (LLM) adaptation, but its high memory cost limits participation from resource-constrained devices. We propose FedPruner, an innovative federated fine-tuning paradigm that tackles this via intelligent layer pruning. FedPruner flexibly prunes the global model, creating personalized submodels based on device memory constraints. It employs a macro-micro synergistic pruning framework: a macro-level functionality-driven layer orche…

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…

Emerging Paradigms for Securing Federated Learning Systems
Amr Akmal Abouelmagd, Amr Hilal
https://arxiv.org/abs/2509.21147 https://arxiv.org/pdf/2509.2114…

Emerging Paradigms for Securing Federated Learning Systems
Federated Learning (FL) facilitates collaborative model training while keeping raw data decentralized, making it a conduit for leveraging the power of IoT devices while maintaining privacy of the locally collected data. However, existing privacy- preserving techniques present notable hurdles. Methods such as Multi-Party Computation (MPC), Homomorphic Encryption (HE), and Differential Privacy (DP) often incur high compu- tational costs and suffer from limited scalability. This survey examines em…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
Yunkai Bao, Reza Safarzadeh, Xin Wang, Steve Drew

MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Wei Wan, Yuxuan Ning, Zhicong Huang, Cheng Hong, Shengshan Hu, Ziqi Zhou, Yechao Zhang, Tianqing Zhu, Wanlei Zhou, Leo Yu Zhang
https://arxiv.org/abs/2509.20383

MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this distributed nature also makes FL highly vulnerable to backdoor attacks. Notably, the recently proposed state-of-the-art (SOTA) attack, 3DFed (SP2023), uses an indicator mechanism to determine whether the backdoor models have been accepted by the defender and adaptively optimizes backdoor models, rendering existing def…

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
Zhiyu Wang, Mohammad Goudarzi, Mingming Gong, Rajkumar Buyya
https://arxiv.org/abs/2508.21328

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
The rapid proliferation of Internet of Things (IoT) applications across heterogeneous Cloud-Edge-IoT environments presents significant challenges in distributed scheduling optimization. Existing approaches face issues, including fixed neural network architectures that are incompatible with computational heterogeneity, non-Independent and Identically Distributed (non-IID) data distributions across IoT scheduling domains, and insufficient cross-domain collaboration mechanisms. This paper proposes…

Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Ren-Yi Huang, Dumindu Samaraweera, Prashant Shekhar, J. Morris Chang
https://arxiv.org/abs/2509.20476

Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping raw data locally stored on client devices, preventing access from other clients or the central server. However, recent studies reveal that sharing model gradients creates vulnerability to Model Inversion Attacks, particularly Deep Leakage from Gradients (DLG), which reconstructs private training data from shared gradients. While Homomorphic Encryption has been proposed as a promising defense me…

Federated Flow Matching
Zifan Wang, Anqi Dong, Mahmoud Selim, Michael M. Zavlanos, Karl H. Johansson
https://arxiv.org/abs/2509.21250 https://arxiv.org/pdf…

Federated Flow Matching
Data today is decentralized, generated and stored across devices and institutions where privacy, ownership, and regulation prevent centralization. This motivates the need to train generative models directly from distributed data locally without central aggregation. In this paper, we introduce Federated Flow Matching (FFM), a framework for training flow matching models under privacy constraints. Specifically, we first examine FFM-vanilla, where each client trains locally with independent source …

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
Zhibo Xu, Jianhao Zhu, Jingwen Xu, Changze Lv, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang
https://arxiv.org/abs/2508.18911

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning settings but also achieves enhanced protection of model parameters compared to baseline methods.

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Md Anwar Hossen, Fatema Siddika, Wensheng Zhang, Anuj Sharma, Ali Jannesari
https://arxiv.org/abs/2508.19009

FedProtoKD: Dual Knowledge Distillation with Adaptive Class-wise Prototype Margin for Heterogeneous Federated Learning
Heterogeneous Federated Learning (HFL) has gained attention for its ability to accommodate diverse models and heterogeneous data across clients. Prototype-based HFL methods emerge as a promising solution to address statistical heterogeneity and privacy challenges, paving the way for new advancements in HFL research. This method focuses on sharing only class-representative prototypes among heterogeneous clients. However, these prototypes are often aggregated on the server using weighted averagin…

Federated Learning based on Self-Evolving Gaussian Clustering
Miha O\v{z}bot, Igor \v{S}krjanc
https://arxiv.org/abs/2508.15393 https://arxiv.org/pdf/2508.…

Federated Learning based on Self-Evolving Gaussian Clustering
In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks…

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…

Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination
Ming Yang, Dongrun Li, Xin Wang, Xiaoyang Yu, Xiaoming Wu, Shibo He
https://arxiv.org/abs/2508.17954

Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination
Cross-client data heterogeneity in federated learning induces biases that impede unbiased consensus condensation and the complementary fusion of generalization- and personalization-oriented knowledge. While existing approaches mitigate heterogeneity through model decoupling and representation center loss, they often rely on static and restricted metrics to evaluate local knowledge and adopt global alignment too rigidly, leading to consensus distortion and diminished model adaptability. To addre…

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Ahmed Mujtaba, Gleb Radchenko, Radu Prodan, Marc Masana
https://arxiv.org/abs/2508.14769

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model outputs (soft logits) rather than full model parameters. However, existing methods employ complex selective knowledge-sharing strategies that require clients to identify in-distribution proxy data through computationally expensive statistical density ratio estimators. Additionally,…

On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Tao Guo, Junxiao Wang, Fushuo Huo, Laizhong Cui, Song Guo, Jie Gui, Dacheng Tao
https://arxiv.org/abs/2508.16261

On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy. Recent research has explored efficient methods for post-training large language models (LLMs) within FL to address computational and communication challenges. While existing approaches often rely on access to LLMs' internal information, which is frequently restricted in real-world scenarios, an inference-only paradigm (black-box FedLLM) has emerged to address these limitations. …