In this paper, we study the challenging task of Byzantine-robust decentralized training on arbitrary communication graphs. Unlike federated learning where workers communicate through a server, workers in the decentralized environment can only talk to their neighbors, making it harder to reach consensus and benefit from collaborative training. To address these issues, we propose a ClippedGossip algorithm for Byzantine-robust consensus and optimization, which is the first to provably converge to a $O(δ_{\max}ζ^2/γ^2)$ neighborhood of the stationary point for non-convex objectives under standard assumptions. Finally, we demonstrate the encouraging empirical performance of ClippedGossip under a large number of attacks.
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
SAMOVAR, Télécom SudParis（SAMOVAR，法国电信南巴黎学院）；Department of Computer Science, German University of Technology in Oman (GUtech)（阿曼技术大学计算机科学系）；L3i, La Rochelle University（拉罗什大学L3i）
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Engineering and Technology, Central Queensland University（中央昆士兰大学工程与技术学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
University of Louisville（路易斯维尔大学）；North Carolina State University（北卡罗来纳州立大学）；University of Wisconsin-Whitewater（威斯康星大学怀特沃特分校）；Wayne State University（韦恩州立大学）；The Ohio State University（俄亥俄州立大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Duke University（杜克大学）
Screen Before You Fetch: Compressed Byzantine Screening for Decentralized Learning on the Edge-Cloud Continuum
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Department of Information and Computer Science, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学信息与计算机科学系）
Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Engineering and Technology, Central Queensland University（中央昆士兰大学工程与技术学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Do We Really Need to Design New Byzantine-robust Aggregation Rules?
Wichita State University（威奇托州立大学）；Rochester Institute of Technology（罗切斯特理工学院）；University of Louisville（路易斯维尔大学）；Florida International University（佛罗里达国际大学）；University of North Texas（北得克萨斯大学）
D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning
Department of Computer Science and Engineering, Indian Institute of Technology, Jodhpur（计算机科学与工程系，印度理工学院，乔浦尔）；School of Mathematics and Computational Sciences, Eastern University Pennsylvania（数学与计算科学学院，东部大学宾夕法尼亚）
On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；Department of Electrical and Computer Engineering, University of California, San Diego（加利福尼亚大学圣地亚哥分校电气与计算机工程系）
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model
Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；College of Computer, National University of Defense Technology（国防科技大学计算机学院）
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Beijing Institute of Technology（北京理工大学）；Southwest University（西南大学）；Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
Department of Electrical and Computer Engineering, University of Windsor（威省大学电气与计算机工程系）；Faculty of Computer Science, University of New Brunswick（新不伦瑞克大学计算机科学学院）
Byzantine-Robust Gossip: Insights from a Dual Approach
Center for Applied Mathematics（应用数学中心）；École polytechnique（巴黎高等理工学院）；IP Paris Palaiseau（巴黎-Palaiseau研究所）；Inria（法国国家信息与自动化技术研究所）；Center of Grenoble Alpes University（格勒诺布尔阿尔卑斯大学中心）；CNRS, LJK（国家科学研究中心，LJK）
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
University of Wisconsin-Whitewater（威斯康星大学怀特沃特分校）；University of Louisville（路易斯维尔大学）；North Carolina State University（北卡罗来纳州立大学）；The Ohio State University（俄亥俄州立大学）
DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
Department of Electrical Engineering, University of South Florida（佛罗里达州立大学电气工程系）；Department of Mathematics, The University of Texas Rio Grande Valley（德克萨斯里奥格兰德谷大学数学系）；Department of Electrical and Computer Engineering, The University of Texas Rio Grande Valley（德克萨斯里奥格兰德谷大学电气与计算机工程系）；Department of Computer Science, Georgia State University（佐治亚州立大学计算机科学系）
A Secure and Private Distributed Bayesian Federated Learning Design
Beijing Laboratory of Advanced Information Network（北京先进信息网络实验室）；Beijing Key Laboratory of Network System Architecture and Convergence（北京网络系统架构与融合重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；College of Information Science and Electronic Engineering（信息科学与电子工程学院）；University of Miami（迈阿密大学）；The University of Hong Kong（香港大学）
Department of Applied Mathematics and Statistics（应用数学与统计学系）；Stony Brook University（石溪大学）；J.P. Morgan AI Research（摩根大通AI研究）；Department of Computer Science（计算机科学系）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Secure and Privacy-Preserving Federated Learning via Co-Utility
UNESCO Chair in Data Privacy（联合国教科文组织数据隐私教席）；CYBERCAT-Center for Cybersecurity Research of Catalonia（CYBERCAT-加泰罗尼亚网络安全研究中心）；Universitat Rovira i Virgili（罗维拉-威尔吉利大学）
Department of Robotics, University of Michigan, Ann Arbor, USA（密歇根大学机器人系，安娜堡，美国）；Thomas Lord Department of Computer Science, University of Southern California, USA（南加州大学计算机科学托马斯·劳德系，美国）
AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息产业部模式分析与机器智能重点实验室）
