Federated Learning is a distributed machine learning framework designed for data privacy preservation i.e., local data remain private throughout the entire training and testing procedure. Federated Learning is gaining popularity because it allows one to use machine learning techniques while preserving privacy. However, it inherits the vulnerabilities and susceptibilities raised in deep learning techniques. For instance, Federated Learning is particularly vulnerable to data poisoning attacks that may deteriorate its performance and integrity due to its distributed nature and inaccessibility to the raw data. In addition, it is extremely difficult to correctly identify malicious clients due to the non-Independently and/or Identically Distributed (non-IID) data. The real-world data can be complex and diverse, making them hardly distinguishable from the malicious data without direct access to the raw data. Prior research has focused on detecting malicious clients while treating only the clients having IID data as benign. In this study, we propose a method that detects and classifies anomalous clients from benign clients when benign ones have non-IID data. Our proposed method leverages feature dimension reduction, dynamic clustering, and cosine similarity-based clipping. The experimental results validates that our proposed method not only classifies the malicious clients but also alleviates their negative influences from the entire procedure. Our findings may be used in future studies to effectively eliminate anomalous clients when building a model with diverse data.
Mind the Cost of Scaffold! Benign Clients May Even Become Accomplices of Backdoor Attack
Nanyang Technological University（南洋理工大学）；Fujian Normal University（福建师范大学）；Huazhong University of Science and Technology（华中科技大学）；Continental Automotive Singapore；Zero Gravity Labs
本文提出 BadSFL，首个针对 Scaffold 的后门攻击，通过篡改控制变量将良性客户端变为帮凶，并利用 GAN 增强投毒，实现超长持久攻击。
Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity
Rajshahi University of Engineering & Technology（拉杰沙希工程与技术大学）；North South University（北南大学）；Missouri State University（密苏里州立大学）；Malardalen University（梅拉达伦大学）；American International University-Bangladesh（孟加拉国美国国际大学）
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
School of Cyberspace Security, Beijing University of Posts and Telecommunications（信息安全部门，北京邮电大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）
Toward Malicious Clients Detection in Federated Learning
Duke University（杜克大学）；Hainan Normal University（海南师范大学）；Wichita State University（威斯康星州立大学）；University of North Texas（北德克萨斯大学）；University of Louisville（路易斯维尔大学）
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）
FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
The University of Manchester, UK（英国曼彻斯特大学）；Institute for Infocomm Research, A*STAR, Singapore（新加坡信息通信研究所）；Monash University, Australia（澳大利亚墨尔本大学）；COSIC, KU Leuven, Belgium（比利时列日大学COSIC实验室）
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）
FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning
School of Cyber Science and Engineering, Wuhan University（武汉大学计算机科学与工程学院）；Department of Computer and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机与人工智能学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）
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）
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算高精尖创新中心）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）
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）
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
The University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德谷分校）；Technical University of Darmstadt（达姆施塔特工业大学）；KOBIL GmbH（KOBIL有限公司）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
Dept. of Computer Science Faculty of Engineering University of Manchester Manchester, United Kingdom；Dept. of Computer Science Faculty of Engineering University of Manchester \& MBZUAI Abu Dhabi, United Arab Emirates；Dept. of Computer Science Faculty of Engineering University of Manchester \& KU Leuven Manchester, United Kingdom
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）
BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models
School of Information and Communication Engineering, Dalian University of Technology（信息与通信工程学院，大连理工大学）；School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation, Chinese Academy of Sciences (CASIA)（模式识别新实验室（NLPR）多模态人工智能系统国家重点实验室（MAIS）自动化研究所，中国科学院（CASIA））
Institute of Computing Technology, Chinese Academy of Sciences, China（中国科学院计算技术研究所）；Purple Mountain Laboratories, China（紫金山实验室）；University of Chinese Academy of Sciences, China（中国科学院大学）
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
University of South Florida（南佛罗里达大学）；University of Massachusetts, Lowell（马萨诸塞大学洛厄尔分校）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；University of Kentucky（肯塔基大学）
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
East China Normal University, Shanghai, China（华东师范大学）；Singapore Management University, Singapore（新加坡管理学院）；Nanyang Technological University, Singapore（南洋理工大学）；National University of Singapore, Singapore（国立新加坡大学）
FAROS: Robust Federated Learning with Adaptive Scaling against Backdoor Attacks
Southwest University（西南大学）；University of Electronic Science and Technology of China（电子科技大学）；Kobe University（Kobe大学）；Zhongguancun Laboratory（中关村实验室）；Waseda University（早稻田大学）
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for 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）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳市人工智能与机器人研究院）
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Nokia Bell Labs（诺基亚贝尔实验室）
