{"total":301,"rows":[{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2108.10241","title":"Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning","created":"2021-08-23","cited_by_count":118,"area":null},{"id":"2311.06750","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","created":"2023-11-12","cited_by_count":99,"area":null},{"id":"2206.10341","title":"Neurotoxin: Durable Backdoors in Federated Learning","created":"2022-06-12","cited_by_count":98,"area":null},{"id":"2106.08283","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","created":"2021-06-15","cited_by_count":91,"area":null},{"id":"2201.08135","title":"Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges","created":"2022-01-20","cited_by_count":84,"area":null},{"id":"2201.00763","title":"DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection","created":"2022-01-03","cited_by_count":81,"area":null},{"id":"2203.08669","title":"MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients","created":"2022-03-16","cited_by_count":68,"area":null},{"id":"2302.01772","title":"Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity","created":"2023-02-03","cited_by_count":65,"area":null},{"id":"2102.01854","title":"Provably Secure Federated Learning against Malicious Clients","created":"2021-02-03","cited_by_count":63,"area":null},{"id":"2205.12173","title":"Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums","created":"2022-05-24","cited_by_count":63,"area":null},{"id":"2210.10936","title":"FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information","created":"2022-10-20","cited_by_count":58,"area":null},{"id":"2406.10416","title":"Byzantine-Robust Decentralized Federated Learning","created":"2024-06-14","cited_by_count":48,"area":null},{"id":"2112.14468","title":"Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning","created":"2021-12-29","cited_by_count":47,"area":null},{"id":"2112.06274","title":"SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification","created":"2021-12-12","cited_by_count":46,"area":null},{"id":"2110.10926","title":"PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion","created":"2021-10-21","cited_by_count":37,"area":null},{"id":"2210.12873","title":"FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning","created":"2022-10-23","cited_by_count":37,"area":null},{"id":"2104.09994","title":"Federated Learning for Malware Detection in IoT Devices","created":"2021-04-15","cited_by_count":36,"area":null},{"id":"2110.13864","title":"FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective","created":"2021-10-26","cited_by_count":36,"area":null},{"id":"2303.02213","title":"Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions","created":"2023-03-03","cited_by_count":33,"area":null},{"id":"2302.07173","title":"An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning","created":"2023-02-14","cited_by_count":32,"area":null},{"id":"2201.02873","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","created":"2022-01-08","cited_by_count":31,"area":null},{"id":"2202.01545","title":"Byzantine-Robust Decentralized Learning via ClippedGossip","created":"2022-02-03","cited_by_count":31,"area":null},{"id":"2307.08925","title":"Integration of Large Language Models and Federated Learning","created":"2023-07-18","cited_by_count":31,"area":null},{"id":"2109.05872","title":"Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering","created":"2021-09-13","cited_by_count":30,"area":null},{"id":"2205.11765","title":"Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees","created":"2022-05-24","cited_by_count":30,"area":null},{"id":"2212.06325","title":"AFLGuard: Byzantine-robust Asynchronous Federated Learning","created":"2022-12-13","cited_by_count":29,"area":null},{"id":"2209.15259","title":"On the Impossible Safety of Large AI Models","created":"2022-09-30","cited_by_count":28,"area":null},{"id":"2210.07714","title":"CrowdGuard: Federated Backdoor Detection in Federated Learning","created":"2022-10-14","cited_by_count":27,"area":null},{"id":"2301.08170","title":"On the Vulnerability of Backdoor Defenses for Federated Learning","created":"2023-01-19","cited_by_count":27,"area":null},{"id":"2308.04604","title":"A Survey on Decentralized Federated Learning","created":"2023-08-08","cited_by_count":27,"area":null},{"id":"2309.13591","title":"Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity","created":"2023-09-24","cited_by_count":26,"area":null},{"id":"2301.00767","title":"A Survey on Federated Recommendation Systems","created":"2022-12-27","cited_by_count":25,"area":null},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24,"area":null},{"id":"2301.09508","title":"BayBFed: Bayesian Backdoor Defense for Federated Learning","created":"2023-01-23","cited_by_count":23,"area":null},{"id":"2303.06601","title":"Multi-metrics adaptively identifies backdoors in Federated learning","created":"2023-03-12","cited_by_count":23,"area":null},{"id":"2304.12961","title":"Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning","created":"2023-04-25","cited_by_count":21,"area":null},{"id":"2306.04959","title":"FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs","created":"2023-06-08","cited_by_count":21,"area":null},{"id":"2106.08882","title":"Robust Training in High Dimensions via Block Coordinate Geometric Median Descent","created":"2021-06-16","cited_by_count":20,"area":null},{"id":"2102.08166","title":"Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?","created":"2021-02-16","cited_by_count":18,"area":null},{"id":"2304.09762","title":"Practical Differentially Private and Byzantine-resilient Federated Learning","created":"2023-04-15","cited_by_count":18,"area":null},{"id":"2308.04466","title":"Backdoor Federated Learning by Poisoning Backdoor-Critical Layers","created":"2023-08-08","cited_by_count":18,"area":null},{"id":"2406.16232","title":"Jacobian Descent for Multi-Objective Optimization","created":"2024-06-23","cited_by_count":18,"area":null},{"id":"2108.03980","title":"Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness","created":"2021-07-30","cited_by_count":15,"area":null},{"id":"2407.06754","title":"Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges","created":"2024-07-09","cited_by_count":15,"area":null},{"id":"2409.01435","title":"Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation","created":"2024-09-02","cited_by_count":15,"area":null},{"id":"2501.17381","title":"Do We Really Need to Design New Byzantine-robust Aggregation Rules?","created":"2025-01-29","cited_by_count":15,"area":null},{"id":"2105.02295","title":"Byzantine-Robust and Privacy-Preserving Framework for FedML","created":"2021-05-05","cited_by_count":14,"area":null},{"id":"2302.06079","title":"Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting","created":"2023-02-13","cited_by_count":14,"area":null},{"id":"2402.01096","title":"Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance","created":"2024-02-02","cited_by_count":14,"area":null},{"id":"2101.10904","title":"Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation","created":"2021-01-24","cited_by_count":13,"area":null},{"id":"2104.06685","title":"BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning","created":"2021-04-14","cited_by_count":13,"area":null},{"id":"2205.00107","title":"Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation","created":"2022-04-29","cited_by_count":13,"area":null},{"id":"2503.07978","title":"Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection","created":"2025-03-11","cited_by_count":13,"area":null},{"id":"2101.05880","title":"Auto-weighted Robust Federated Learning with Corrupted Data Sources","created":"2021-01-14","cited_by_count":12,"area":null},{"id":"2106.11257","title":"Secure Distributed Training at Scale","created":"2021-06-21","cited_by_count":12,"area":null},{"id":"2209.04030","title":"Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks","created":"2022-09-08","cited_by_count":12,"area":null},{"id":"2310.09804","title":"Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates","created":"2023-10-15","cited_by_count":12,"area":null},{"id":"2107.07334","title":"Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments","created":"2021-05-29","cited_by_count":11,"area":null},{"id":"2302.13473","title":"Towards Interpretable Federated Learning","created":"2023-02-27","cited_by_count":11,"area":null},{"id":"2306.08393","title":"Provably Personalized and Robust Federated Learning","created":"2023-06-14","cited_by_count":11,"area":null},{"id":"2403.03149","title":"Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks","created":"2024-03-05","cited_by_count":11,"area":null},{"id":"2411.01583","title":"Trustworthy Federated Learning: Privacy, Security, and Beyond","created":"2024-11-03","cited_by_count":11,"area":null},{"id":"2304.08847","title":"BadVFL: Backdoor Attacks in Vertical Federated Learning","created":"2023-04-18","cited_by_count":10,"area":null},{"id":"2304.10783","title":"Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning","created":"2023-04-21","cited_by_count":10,"area":null},{"id":"2403.19326","title":"MedBN: Robust Test-Time Adaptation against Malicious Test Samples","created":"2024-03-28","cited_by_count":10,"area":null},{"id":"2405.15182","title":"RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation","created":"2024-05-24","cited_by_count":10,"area":null},{"id":"2107.08402","title":"RobustFed: A Truth Inference Approach for Robust Federated Learning","created":"2021-07-18","cited_by_count":9,"area":null},{"id":"2109.03999","title":"Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies","created":"2021-09-09","cited_by_count":9,"area":null},{"id":"2203.11633","title":"Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis","created":"2022-03-22","cited_by_count":9,"area":null},{"id":"2301.09732","title":"Backdoor Attacks in Peer-to-Peer Federated Learning","created":"2023-01-23","cited_by_count":9,"area":null},{"id":"2303.03908","title":"Client-specific Property Inference against Secure Aggregation in Federated Learning","created":"2023-03-07","cited_by_count":9,"area":null},{"id":"2305.18285","title":"Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity","created":"2023-05-29","cited_by_count":9,"area":null},{"id":"2311.16065","title":"A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective","created":"2023-11-27","cited_by_count":9,"area":null},{"id":"2107.01477","title":"Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates","created":"2021-07-03","cited_by_count":8,"area":null},{"id":"2110.04350","title":"FRL: Federated Rank Learning","created":"2021-10-08","cited_by_count":8,"area":null},{"id":"2207.10804","title":"Suppressing Poisoning Attacks on Federated Learning for Medical Imaging","created":"2022-07-15","cited_by_count":8,"area":null},{"id":"2208.09894","title":"Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning","created":"2022-08-21","cited_by_count":8,"area":null},{"id":"2309.03437","title":"Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy","created":"2023-09-07","cited_by_count":8,"area":null},{"id":"2310.13424","title":"FLTracer: Accurate Poisoning Attack Provenance in Federated Learning","created":"2023-10-20","cited_by_count":8,"area":null},{"id":"2406.01960","title":"Certifiably Byzantine-Robust Federated Conformal Prediction","created":"2024-06-04","cited_by_count":8,"area":null},{"id":"2108.11769","title":"Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy","created":"2021-08-26","cited_by_count":7,"area":null},{"id":"2209.14547","title":"A Secure Federated Learning Framework for Residential Short Term Load Forecasting","created":"2022-09-29","cited_by_count":7,"area":null},{"id":"2308.03573","title":"When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection","created":"2023-08-07","cited_by_count":7,"area":null},{"id":"2401.08216","title":"Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning","created":"2024-01-16","cited_by_count":7,"area":null},{"id":"2405.08698","title":"Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises","created":"2024-05-14","cited_by_count":7,"area":null},{"id":"2110.09660","title":"BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers","created":"2021-10-18","cited_by_count":6,"area":null},{"id":"2112.10525","title":"Certified Federated Adversarial Training","created":"2021-12-20","cited_by_count":6,"area":null},{"id":"2205.04134","title":"Federated Multi-Armed Bandits Under Byzantine Attacks","created":"2022-05-09","cited_by_count":6,"area":null},{"id":"2206.12100","title":"zPROBE: Zero Peek Robustness Checks for Federated Learning","created":"2022-06-24","cited_by_count":6,"area":null},{"id":"2303.03829","title":"Can Decentralized Learning be more robust than Federated Learning?","created":"2023-03-07","cited_by_count":6,"area":null},{"id":"2303.10434","title":"Byzantine-Resilient Federated Learning at Edge","created":"2023-03-18","cited_by_count":6,"area":null},{"id":"2311.06801","title":"A Comprehensive Survey On Client Selections in Federated Learning","created":"2023-11-12","cited_by_count":6,"area":null},{"id":"2312.04542","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2505.04873","title":"Federated Learning for Cyber Physical Systems: A Comprehensive Survey","created":"2025-05-08","cited_by_count":6,"area":null},{"id":"2207.07941","title":"MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks","created":"2022-07-16","cited_by_count":5,"area":null},{"id":"2208.10161","title":"MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering","created":"2022-08-22","cited_by_count":5,"area":null},{"id":"2211.07902","title":"Byzantine Spectral Ranking","created":"2022-11-15","cited_by_count":5,"area":null},{"id":"2212.01976","title":"FedCC: Robust Federated Learning against Model Poisoning Attacks","created":"2022-12-05","cited_by_count":5,"area":null},{"id":"2311.06996","title":"AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification","created":"2023-11-13","cited_by_count":5,"area":null}],"next_cursor":"100"}