Tootfinder

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Nina Cai, Jinguang Han
https://arxiv.org/abs/2506.12846

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malici…

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Haolong Jin, Shenglin Liu, Cong Cong, Qingmin Feng, Yongzhi Liu, Lina Huang, Yingzi Hu
https://arxiv.org/abs/2506.15365

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserving sensitive patient data. In the context of Whole Slide Image (WSI) classification, FL faces significant challenges, including heterogeneous computational resources across participating medical institutes and privacy concerns. To address these challenges, we propose FedWSIDD, a novel FL paradigm that leverages datas…

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
Pranjal Naman, Yogesh Simmhan
https://arxiv.org/abs/2506.12425 https://

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. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging parallelism. Existing methods that address the unique requirements of federated GNN training using remote embeddings to enhance convergence accuracy are limited by…

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Zhiqiang Li, Haiyong Bao, Menghong Guan, Hao Pan, Cheng Huang, Hong-Ning Dai
https://arxiv.org/abs/2506.13612

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and R…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate...
Anshu Ankolekar, et al.

Latency Optimization for Wireless Federated Learning in Multihop Networks
Shaba Shaon, Van-Dinh Nguyen, Dinh C. Nguyen
https://arxiv.org/abs/2506.12081 htt…

Latency Optimization for Wireless Federated Learning in Multihop Networks
In this paper, we study a novel latency minimization problem in wireless federated learning (FL) across multi-hop networks. The system comprises multiple routes, each integrating leaf and relay nodes for FL model training. We explore a personalized learning and adaptive aggregation-aware FL (PAFL) framework that effectively addresses data heterogeneity across participating nodes by harmonizing individual and collective learning objectives. We formulate an optimization problem aimed at minimizin…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/8]:
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Nina Cai, Jinguang Han
https://arxiv.org/abs/2506.12846

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malici…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Supporting the development of Machine Learning for fundamental science in a federated Cloud with ...
Lucio Anderlini, et al.

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Zhiqiang Li, Haiyong Bao, Menghong Guan, Hao Pan, Cheng Huang, Hong-Ning Dai
https://arxiv.org/abs/2506.13612

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and R…

Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition
Tao Zhong, Mengzhe Geng, Shujie Hu, Guinan Li, Xunying Liu
https://arxiv.org/abs/2506.11069

Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition
Accurate recognition of dysarthric and elderly speech remains challenging to date. While privacy concerns have driven a shift from centralized approaches to federated learning (FL) to ensure data confidentiality, this further exacerbates the challenges of data scarcity, imbalanced data distribution and speaker heterogeneity. To this end, this paper conducts a systematic investigation of regularized FL techniques for privacy-preserving dysarthric and elderly speech recognition, addressing differ…

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2506.04626

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a focus on minimizing burn-in costs (the sample sizes needed to reach near-optimal regret) and policy switching or communication costs. In parallel finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states and $A$ actions, existing m…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/8]:
Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Ana...

Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Yanjie Dong, Haijun Zhang, Gaojie Chen, Xiaoyi Fan, Victor C. M. Leung, Xiping Hu
https://arxiv.org/abs/2506.04113

Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Recent advancements have introduced federated machine learning-based channel state information (CSI) compression before the user equipments (UEs) upload the downlink CSI to the base transceiver station (BTS). However, most existing algorithms impose a high communication overhead due to frequent parameter exchanges between UEs and BTS. In this work, we propose a model splitting approach with a shared model at the BTS and multiple local models at the UEs to reduce communication overhead. Moreover…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new/
[1/3]:
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal...

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Huong Nguyen, Hong-Tri Nguyen, Praveen Kumar Donta, Susanna Pirttikangas, Lauri Lov\'en
https://arxiv.org/abs/2506.10607

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Federated learning has emerged as a privacy-preserving technique for collaborative model training across heterogeneously distributed silos. Yet, its reliance on a single central server introduces potential bottlenecks and risks of single-point failure. Decentralizing the server, often referred to as decentralized learning, addresses this problem by distributing the server role across nodes within the network. One drawback regarding this pure decentralization is it introduces communication ineff…

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms
Zijun Zhan, Yaxian Dong, Daniel Mawunyo Doe, Yuqing Hu, Shuai Li, Shaohua Cao, Zhu Han
https://arxiv.org/abs/2506.01285

A Reliable Vertical Federated Learning Framework for Traffic State Estimation with Data Selection and Incentive Mechanisms
Vertical Federated Learning (VFL)-based Traffic State Estimation (TSE) offers a promising approach for integrating vertically distributed traffic data from municipal authorities (MA) and mobility providers (MP) while safeguarding privacy. However, given the variations in MPs' data collection capabilities and the potential for MPs to underperform in data provision, we propose a reliable VFL-based TSE framework that ensures model reliability during training and operation. The proposed framework c…

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Hariharan Ramesh, Jyotikrishna Dass
https://arxiv.org/abs/2506.09199

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of Large Language Models (LLMs) without sharing local data. However, several methods designed for federated LoRA present significant challenges in balancing communication efficiency, model accuracy, and computational cost, particularly among heterogeneous clients. These methods either rely on simplistic averaging of local adapters, which introduces aggregation noise, re…

FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
Jun Bai, Rajib Rana, Di Wu, Youyang Qu, Xiaohui Tao, Ji Zhang
https://arxiv.org/abs/2506.10207

A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck
Ciro Benito Raggio, Paolo Zaffino, Maria Francesca Spadea
https://arxiv.org/abs/2506.08654

A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck
Shortened Abstract Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT suffers from increased noise, limited soft-tissue contrast, and artifacts, resulting in unreliable Hounsfield unit values and hindering direct dose calculation. Synthetic CT (sCT) generation from CBCT addresses these issues, especially using deep learning (DL) methods. Existing approaches are limited by institutional heterogeneity, scanner-dependent …

Replaced article(s) found for cs.CC. https://arxiv.org/list/cs.CC/new/
[1/1]:
Privacy-aware Berrut Approximated Coded Computing for Federated Learning

Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
Roopkatha Banerjee, Tejus Chandrashekar, Ananth Eswar, Yogesh Simmhan
https://arxiv.org/abs/2506.10413

Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, optimizing both energy efficiency and model accuracy remains a challenge, given device and data heterogeneity. Further, sustainable AI through a global energy budget for FL has not been explored. We propose a novel optimization problem for client selection in FL that maximizes the model accuracy within an overall energy limit and reduces training time. We solve this wi…

This https://arxiv.org/abs/2503.04091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalization in Federated Learning: A Conditional Mutual Information Framework
Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized learning. In FL, the generalization error consists of two components: the out-of-sample gap, which measures the gap between the empirical and true risk for participating clients, and the participation gap, which quantifies the risk difference between participating and non-participating clients. In this work, we apply an i…

Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM
Xun Li, Qiong Wu
https://arxiv.org/abs/2506.04593 https://

Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM
Edge caching is an emerging technology that empowers caching units at edge nodes, allowing users to fetch contents of interest that have been pre-cached at the edge nodes. The key to pre-caching is to maximize the cache hit percentage for cached content without compromising users' privacy. In this letter, we propose a federated learning (FL) assisted edge caching scheme based on lightweight architecture denoising diffusion probabilistic model (LDPM). Our simulation results verify that our propo…

Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity
Jun-Pyo Hong, Hyowoon Seo, Kisong Lee
https://arxiv.org/abs/2506.06090

Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity
The conventional FL methods face critical challenges in realistic wireless edge networks, where training data is both limited and heterogeneous, often leading to unstable training and poor generalization. To address these challenges in a principled manner, we propose a novel wireless FL framework grounded in Bayesian inference. By virtue of the Bayesian approach, our framework captures model uncertainty by maintaining distributions over local weights and performs distribution-level aggregation …

pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
Mrinmay Sen, Chalavadi Krishna Mohan
https://arxiv.org/abs/2506.07159

pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models tailored to their individual objectives, addressing the challenge of model generalization in traditional Federated Learning (FL) due to high data heterogeneity. However, existing PFL methods often require increased communication rounds to achieve the desired performance, primarily due to slow training caused by the use of first-order optimization, which has linear convergence. Additionally, many o…

This https://arxiv.org/abs/2503.09649 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics
Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have the same impact in bioinformatics, allowing access to many combinations of genotypic, pheno…

This https://arxiv.org/abs/2504.12849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
Federated Learning (FL) allows collaborative training among multiple devices without data sharing, thus enabling privacy-sensitive applications on mobile or Internet of Things (IoT) devices, such as mobile health and asset tracking. However, designing an FL system with good model utility that works with low computation/communication overhead on heterogeneous, resource-constrained mobile/IoT devices is challenging. To address this problem, this paper proposes FedX, a novel adaptive model decompo…

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
Gabriele Digregorio, Francesco Bleggi, Federico Caroli, Michele Carminati, Stefano Zanero, Stefano Longari
https://arxiv.org/abs/2506.02679

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluate system robustness under adversarial conditions. To fill this gap, we present FedBlockParadox, a modular framework for modeling and evaluating decentralized federated learning systems built on blockcha…

This https://arxiv.org/abs/2504.13880 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study
Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture…

This https://arxiv.org/abs/2505.04947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prot…

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Hasin Us Sami, Swapneel Sen, Amit K. Roy-Chowdhury, Srikanth V. Krishnamurthy, Basak Guler
https://arxiv.org/abs/2506.04453

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning (PEFT) of large-scale pretrained models has gained substantial attention in FL. While keeping a pretrained (backbone) model frozen, each user fine-tunes only a few lightweight modules to be used in conjunction, to fit speci…

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
Gabriele Digregorio, Elisabetta Cainazzo, Stefano Longari, Michele Carminati, Stefano Zanero
https://arxiv.org/abs/2506.04978

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning (FL) solutions in the vehicular intrusion detection domain. In this paper, we investigate the effects of integrating FL strategies into the machine learning-based intrusion detection process for on-board vehicular networks. Accordingly, we propose a …

This https://arxiv.org/abs/2505.17226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm…

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
Utku Demir, Yalin E. Sagduyu, Tugba Erpek, Hossein Jafari, Sastry Kompella, Mengran Xue
https://arxiv.org/abs/2505.23706

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
In connected and autonomous vehicles, machine learning for safety message classification has become critical for detecting malicious or anomalous behavior. However, conventional approaches that rely on centralized data collection or purely local training face limitations due to the large scale, high mobility, and heterogeneous data distributions inherent in inter-vehicle networks. To overcome these challenges, this paper explores Distributed Federated Learning (DFL), whereby vehicles collaborat…

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee, Marc Vucovich, Kevin Choi, Abdul Rahman, Alison Hu, Edward Bowen, Sachin Shetty
https://arxiv.org/abs/2505.23791

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extraction (ME) attacks pose a significant risk to Machine Learning as a Service (MLaaS) platforms, enabling attackers to replicate confidential models by querying black-box (without internal insight) APIs. Despite FL's privacy-preserving goals, its distributed nature makes it particularly susceptible to such attacks. This p…

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Xiyu Zhao, Qimei Cui, Ziqiang Du, Weicai Li, Xi Yu, Wei Ni, Ji Zhang, Xiaofeng Tao, Ping Zhang
https://arxiv.org/abs/2506.02422

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). Little attention has been given to wireless PFL (WPFL), where privacy concerns arise. Performance fairness of PL models is another challenge resulting from communication bottlenecks in WPFL. This paper exploits quantization errors to enhance the privacy of WPFL and proposes a novel quantization-assisted Gaussian differ…

This https://arxiv.org/abs/2503.20117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
This work tackles the fundamental challenges in Federated Learning (FL) posed by arbitrary client participation and data heterogeneity, prevalent characteristics in practical FL settings. It is well-established that popular FedAvg-style algorithms struggle with exact convergence and can suffer from slow convergence rates since a decaying learning rate is required to mitigate these scenarios. To address these issues, we introduce the concept of stochastic matrix and the corresponding time-varyin…

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri, Suren Byna
https://arxiv.org/abs/2505.23849

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves privacy and security of the client's data by not exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data REadiness) for FL, a novel framework that allows users to define…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
Advancing Hybrid Defense for Byzantine Attacks in Federated Learning
htt…

This https://arxiv.org/abs/2505.01874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Towards Trustworthy Federated Learning with Untrusted Participants
Resilience against malicious participants and data privacy are essential for trustworthy federated learning, yet achieving both with good utility typically requires the strong assumption of a trusted central server. This paper shows that a significantly weaker assumption suffices: each pair of participants shares a randomness seed unknown to others. In a setting where malicious participants may collude with an untrusted server, we propose CafCor, an algorithm that integrates robust gradient agg…

Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration
Faisal Ahmed, Myungjin Lee, Shao-Yu Lien, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih-Chun Lin
https://arxiv.org/abs/2506.00822

Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration
The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the …

This https://arxiv.org/abs/2503.06554 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

BDPFL: Backdoor Defense for Personalized Federated Learning via Explainable Distillation
Federated learning is a distributed learning paradigm that facilitates the collaborative training of a global model across multiple clients while preserving the privacy of local datasets. To address inherent challenges related to data heterogeneity and satisfy personalized needs, a new direction within FL, known as personalized Federated Learning (pFL), has gradually evolved. Extensive attention has been directed toward developing novel frameworks and methods to enhance the performance of pFL. …

This https://arxiv.org/abs/2505.12453 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
Federated recommender system (FedRec) has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full model and entire weight updates between edge devices and the server, causing significant burdens to devices with limited bandwidth and computational power. While the sparsity of embedding updates provides opportunity for payload optimization, existing sparsity-aware federated protocols generally sacrifice privacy for effi…

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
Xiaopei Chen, Liang Li, Fei Ji, Wen Wu
https://arxiv.org/abs/2506.02940

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
In this paper, we propose an edge-assisted split federated learning framework to facilitate large language model (LLM) fine-tuning on heterogeneous mobile devices while alleviating memory pressures on both mobile devices and the edge server. Specifically, mobile devices perform low-rank adaptation (LoRA) fine-tuning on only a subset of lower layers of the pre-trained LLM, tailored to their individual capacities. On the server, a full LLM is maintained, and the corresponding LoRA modules are sel…

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries
Abhijit Talluri
https://arxiv.org/abs/2505.23813

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries
Federated Learning (FL) has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system resilience against concurrent client and server failures, and the provision of robust, verifiable privacy guarantees essential for handling sensitive data. These deficiencies can lead to training disruption…

EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu, Wenjing Lou, Chongjie Zhang, William Yeoh, Yevgeniy Vorobeychik, Ning Zhang
https://arxiv.org/abs/2506.02001

EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on various downstream tasks without requiring aggregated data. However, the repeated exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. To address this challenge, we propose EcoLoRA, a novel communication-efficient federated fine-tuning framework…

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
Md Jueal Mia, M. Hadi Amini
https://arxiv.org/abs/2506.05640 https://…

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
Federated Learning (FL) offers a decentralized framework for training and fine-tuning Large Language Models (LLMs) by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associated with the substantial computational demands of LLMs, which can be prohibitive for small and medium-sized organizations. FL supports the development of task-specific LLMs for cross-silo applications…

This https://arxiv.org/abs/2505.07614 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy
Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their structure is vulnerable to malicious influences. In this paper, we address a specific threat, Byzantine attacks, where compromised clients inject adversarial updates to derail global convergence. We combine the trust scores concept with trial function methodology to dynamically filter outliers. Our methods address the c…

This https://arxiv.org/abs/2501.12911 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However, current security implementations for these systems face a fundamental trade-off: rigorous cryptographic protections like fully homomorphic encryption (FHE) impose prohibitive computational overhead, while lightweight alternatives risk vulnerable data leakage th…

This https://arxiv.org/abs/2505.24603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its inherent randomness. In this work, we revisit this operation through the lens of Renyi Differential Privacy (RDP), providing a refined pri…

This https://arxiv.org/abs/2503.00140 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approaching the Harm of Gradient Attacks While Only Flipping Labels
Machine learning systems deployed in distributed or federated environments are highly susceptible to adversarial manipulations, particularly availability attacks -adding imperceptible perturbations to training data, thereby rendering the trained model unavailable. Prior research in distributed machine learning has demonstrated such adversarial effects through the injection of gradients or data poisoning. In this study, we aim to enhance comprehension of the potential of weaker (and more probabl…