Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great research interest with specific strategies developed, no known solution manages to address them in a unified framework. To universally overcome both challenges, we propose SmartFL, a generic approach that optimizes the server-side aggregation process with a small amount of proxy data collected by the service provider itself via a subspace training technique. Specifically, the aggregation weight of each participating client at each round is optimized using the server-collected proxy data, which is essentially the optimization of the global model in the convex hull spanned by client models. Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data (e.g., around one hundred samples). With optimized aggregation, SmartFL ensures robustness against both heterogeneous and malicious clients, which is desirable in real-world FL where either or both problems may occur. We provide theoretical analyses of the convergence and generalization capacity for SmartFL. Empirically, SmartFL achieves state-of-the-art performance on both FL with non-IID data distribution and FL with malicious clients. The source code will be released.
Poisoning Attacks and Defenses to Federated Unlearning
ShanghaiTech University（上海科技大学）；Xidian University（西安电子科技大学）；North Carolina State University（北卡罗来纳州立大学）；University of North Texas（北德克萨斯大学）；University of Louisville（路易斯维尔大学）
Data Poisoning Attacks on Federated Machine Learning
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Rochester（罗切斯特大学）
Data Poisoning and Leakage Analysis in Federated Learning
Georgia Institute of Technology（佐治亚理工学院）；Fordham University（福特汉姆大学）；National Institute of Standards and Technology（美国国家标准与技术研究院）；Georgia Tech Research Institute（佐治亚理工研究院）
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
School of Computing, KAIST（韩国科学技术院计算机学院）；Samsung Research（三星研究院）；Data Science Group, Institute for Basic Science（基础科学研究院数据科学组）；Max Planck Institute for Security and Privacy（马克斯·普朗克安全与隐私研究所）
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Meta Platforms（Meta平台公司）；University of Science and Technology of China（中国科学技术大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Duke University（杜克大学）
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
Nanjing University of Science and Technology（南京理工大学）；National Research Tomsk Polytechnic University（托木斯克国立研究理工大学）；Data61, CSIRO（联邦科学与工业研究组织Data61）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；Princeton University（普林斯顿大学）
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
Norwegian University of Science and Technology（挪威科技大学）；Aalto University（阿尔托大学）；Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；University of Southern Denmark（南丹麦大学）
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）
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）；Swinburne University of Technology（斯威本科技大学）；Griffith University（格里菲斯大学）；RMIT University（皇家墨尔本理工大学）；Xiangtan University（湘潭大学）
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
Honda Research Institute EU（本田欧洲研究所）；University of Kent（肯特大学）；University of Lille（里尔大学）；ENSAIT；Dong A University（东亚大学）；International Research Institute for Artificial Intelligence and Data Science（人工智能与数据科学国际研究院）
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
Queen Mary University of London（伦敦玛丽女王大学）；University of Oxford（牛津大学）；Huazhong University of Science and Technology（华中科技大学）；Shandong University of Technology（山东理工大学）
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）
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）
FedLAD: A Linear Algebra Based Data Poisoning Defence for Federated Learning
School of Computing Technologies, Centre of Cyber Security Research and Innovation (CCSRI), RMIT University（计算技术学院，网络安全研究与创新中心（CCSRI），皇家墨尔本理工学院）；School of Computing Technologies, Centre of Cyber Security Research（计算技术学院，网络安全研究中心）；Cyber Security discipline, School of Information Technologies, Deakin University（网络安全学科，信息科技学院，迪金大学）
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）
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）
Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Cyber Science and Technology at Beihang University（北京航空航天大学网络安全科学与技术学院）；School of Cyberspace Science and Technology, Beijing Jiaotong University（北京交通大学网络空间科学与技术学院）；Beijing Key Laboratory of Security and Privacy in Intelligent Transportation（北京智能交通安全与隐私重点实验室）；Zhongguancun Laboratory（中关村实验室）；School of Computing and Information,University of Pittsburgh（匹兹堡大学计算与信息学院）
