Federated learning (FL) has garnered significant attention as a prominent privacy-preserving Machine Learning (ML) paradigm. Decentralized FL (DFL) eschews traditional FL's centralized server architecture, enhancing the system's robustness and scalability. However, these advantages of DFL also create new vulnerabilities for malicious participants to execute adversarial attacks, especially model poisoning attacks. In model poisoning attacks, malicious participants aim to diminish the performance of benign models by creating and disseminating the compromised model. Existing research on model poisoning attacks has predominantly concentrated on undermining global models within the Centralized FL (CFL) paradigm, while there needs to be more research in DFL. To fill the research gap, this paper proposes an innovative model poisoning attack called DMPA. This attack calculates the differential characteristics of multiple malicious client models and obtains the most effective poisoning strategy, thereby orchestrating a collusive attack by multiple participants. The effectiveness of this attack is validated across multiple datasets, with results indicating that the DMPA approach consistently surpasses existing state-of-the-art FL model poisoning attack strategies.
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
Center for Data Science, Zhejiang University, Hangzhou 310000, China（数据科学中心，浙江大学，杭州）；College of Computer Science, Zhejiang University, Hangzhou 310000, China（计算机科学学院，浙江大学，杭州）
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）
Department of Computer Science, University of Oxford, Oxford, UK；Newcastle University, UK；Hong Kong University of Science；HongKong Generative AI Research \& Development Center, Hong Kong SAR
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Jinan University（暨南大学）
GFL: A Decentralized Federated Learning Framework Based On Blockchain
Polytechnic Institute, Zhejiang University（浙江大学理工学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Public Affairs, Zhejiang University（浙江大学公共管理学院）
Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous 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）；School of Engineering and Applied Science, Harvard University（哈佛大学工程与应用科学学院）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电子与电气工程系）
Poisoning Attacks on Federated Learning for Autonomous Driving
Umeå University（于默奥大学）；AI Sweden（瑞典人工智能研究院）；Royal Institute of Technology（皇家理工学院）；Chalmers University of Technology（查尔姆斯理工大学）；Dakota State University（达科他州立大学）；Scaleout Systems（Scaleout系统公司）
Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；The Hong Kong University of Science and Technology（香港科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning
University at Albany（阿尔巴尼大学）；Meta Inc.（Meta公司）；New Jersey Institute of Technology（新泽西理工学院）；Qatar Computing Research Institute（卡塔尔计算研究院）；University of Arkansas（阿肯色大学）
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks
Ravensbourne University London (RUL)（伦敦 Ravensbourne 大学）；Bahcesehir University (BAU)（巴赫切谢希尔大学）；University of Portsmouth（朴茨茅斯大学）；University of Padua（帕多瓦大学）；Delft University of Technology（代尔夫特理工大学）
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
National Yang Ming Chiao Tung University（国立阳明交通大学）；RIKEN AIP（理化学研究所人工智能项目）；ISM and AIST（信息科学研究机构与产业技术综合研究所）；Institute of Science Tokyo（东京科学大学）；Meiji University（明治大学）
GShield: Mitigating Poisoning Attacks in Federated Learning
Department of Computer Science and Engineering, JAIN (Deemed-to-be University), Kochi, India（计算机科学与工程系，贾因大学（认定大学），印度科钦）；Department of Science and Technological Innovation, University of Eastern Piedmont, V.le Teresa Michel, 11, Alessandria, Italy（科学技术创新系，东皮埃蒙特大学，意大利艾利斯安迪亚）；Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy（电气、计算机与生物医学工程系，皮埃蒙特大学，意大利）；Department of Computer Applications, Cochin University of Science and Technology, India（计算机应用系，科钦科学与技术大学，印度）
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
Department of Computer Applications, Cochin University of Science and Technology（科钦大学科学与技术学院计算机应用系）；Department of Mathematics, University of Padua（帕多瓦大学数学系）；University of Campinas (Unicamp)（坎皮纳斯大学）
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）
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）
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）
FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
Zhejiang University（浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；University of Toronto（多伦多大学）
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
1 Department of Multidisciplinary Engineering, Texas A\&M University, College Station, TX, USA；2 Department of Statistics；3 Department of Computer Engineering, KNUST, Kumasi, Ghana；4 Department of Computer Science, George Washington University, Washington, DC, USA；5 Department of Engineering Technology；Industrial Distribution, Texas A\&M University, College Station, TX, USA
