{"total":88,"rows":[{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2207.09209","title":"FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients","created":"2022-07-19","cited_by_count":125,"area":null},{"id":"2206.10341","title":"Neurotoxin: Durable Backdoors in Federated Learning","created":"2022-06-12","cited_by_count":98,"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":"2310.20448","title":"A Survey on Federated Unlearning: Challenges, Methods, and Future Directions","created":"2023-10-31","cited_by_count":63,"area":"safety"},{"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":"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":"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":"2201.02873","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","created":"2022-01-08","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":"2401.17319","title":"Decentralized Federated Learning: A Survey on Security and Privacy","created":"2024-01-25","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":"2312.08667","title":"Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey","created":"2023-12-14","cited_by_count":18,"area":null},{"id":"2406.05670","title":"Certified Robustness to Data Poisoning in Gradient-Based Training","created":"2024-06-09","cited_by_count":18,"area":"safety"},{"id":"2102.05257","title":"Robust Federated Learning with Attack-Adaptive Aggregation","created":"2021-02-10","cited_by_count":17,"area":"safety"},{"id":"2108.00365","title":"A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee","created":"2021-08-01","cited_by_count":16,"area":null},{"id":"2305.05506","title":"FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation","created":"2023-05-09","cited_by_count":16,"area":null},{"id":"2106.02310","title":"FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning","created":"2021-06-04","cited_by_count":14,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"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":"2204.03500","title":"Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation","created":"2022-04-07","cited_by_count":10,"area":null},{"id":"2310.09789","title":"FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy","created":"2023-10-15","cited_by_count":10,"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":"2108.10761","title":"Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health","created":"2021-08-24","cited_by_count":8,"area":null},{"id":"2208.10273","title":"Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning","created":"2022-08-14","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":"2309.16456","title":"Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective","created":"2023-09-28","cited_by_count":6,"area":null},{"id":"2502.17526","title":"FedSV: Byzantine-Robust Federated Learning via Shapley Value","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2212.00322","title":"Hijack Vertical Federated Learning Models As One Party","created":"2022-12-01","cited_by_count":5,"area":null},{"id":"2409.20329","title":"Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients","created":"2024-09-30","cited_by_count":5,"area":null},{"id":"2109.00202","title":"Federated Learning: Issues in Medical Application","created":"2021-09-01","cited_by_count":4,"area":null},{"id":"2110.11006","title":"Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments","created":"2021-10-21","cited_by_count":4,"area":null},{"id":"2207.02337","title":"Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2302.06801","title":"Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions","created":"2023-02-14","cited_by_count":4,"area":"safety"},{"id":"2305.11537","title":"Trustworthy Federated Learning: A Survey","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2406.14362","title":"Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2602.16944","title":"Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming","created":"2026-02-18","cited_by_count":4,"area":"safety"},{"id":"2207.12327","title":"Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment","created":"2022-07-25","cited_by_count":3,"area":null},{"id":"2309.14148","title":"SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture","created":"2023-09-25","cited_by_count":3,"area":null},{"id":"2404.14933","title":"Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data","created":"2024-04-23","cited_by_count":3,"area":null},{"id":"2407.09658","title":"BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning","created":"2024-07-12","cited_by_count":3,"area":null},{"id":"2502.11863","title":"FedEAT: A Robustness Optimization Framework for Federated LLMs","created":"2025-02-17","cited_by_count":3,"area":null},{"id":"2508.05224","title":"Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning","created":"2025-08-07","cited_by_count":3,"area":null},{"id":"2512.17254","title":"Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning","created":"2025-12-19","cited_by_count":3,"area":null},{"id":"2105.08671","title":"DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities","created":"2021-05-18","cited_by_count":2,"area":null},{"id":"2108.04551","title":"ABC-FL: Anomalous and Benign client Classification in Federated Learning","created":"2021-08-10","cited_by_count":2,"area":null},{"id":"2111.14434","title":"Robust Federated Learning for execution time-based device model identification under label-flipping attack","created":"2021-11-29","cited_by_count":2,"area":null},{"id":"2211.05739","title":"FedLesScan: Mitigating Stragglers in Serverless Federated Learning","created":"2022-11-10","cited_by_count":2,"area":null},{"id":"2310.04055","title":"Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification","created":"2023-10-06","cited_by_count":2,"area":"safety"},{"id":"2403.02846","title":"FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models","created":"2024-03-05","cited_by_count":2,"area":null},{"id":"2405.08044","title":"On the Volatility of Shapley-Based Contribution Metrics in Federated Learning","created":"2024-05-13","cited_by_count":2,"area":null},{"id":"2409.19934","title":"Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition","created":"2024-09-30","cited_by_count":2,"area":null},{"id":"2506.09674","title":"Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning","created":"2025-06-11","cited_by_count":2,"area":null},{"id":"2106.06891","title":"Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning","created":"2021-06-13","cited_by_count":1,"area":null},{"id":"2109.02396","title":"Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data","created":"2021-09-06","cited_by_count":1,"area":null},{"id":"2202.04311","title":"Identifying Backdoor Attacks in Federated Learning via Anomaly Detection","created":"2022-02-09","cited_by_count":1,"area":null},{"id":"2208.01826","title":"A New Implementation of Federated Learning for Privacy and Security Enhancement","created":"2022-08-03","cited_by_count":1,"area":null},{"id":"2310.00616","title":"Towards Understanding Adversarial Transferability in Federated Learning","created":"2023-10-01","cited_by_count":1,"area":null},{"id":"2405.13832","title":"Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2406.04702","title":"Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems","created":"2024-06-07","cited_by_count":1,"area":null},{"id":"2407.07917","title":"Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape","created":"2024-07-05","cited_by_count":1,"area":null},{"id":"2409.13004","title":"Data Poisoning and Leakage Analysis in Federated Learning","created":"2024-09-19","cited_by_count":1,"area":"safety"},{"id":"2505.12019","title":"FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2508.01669","title":"Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models","created":"2025-08-03","cited_by_count":1,"area":null},{"id":"2508.02414","title":"ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning","created":"2025-08-04","cited_by_count":1,"area":null},{"id":"2508.13853","title":"FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks","created":"2025-08-19","cited_by_count":1,"area":"safety"},{"id":"2508.18737","title":"FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2509.00634","title":"Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats","created":"2025-08-30","cited_by_count":1,"area":null},{"id":"2609.15681","title":"Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis","created":"2026-09-14","cited_by_count":1,"area":null},{"id":"2110.11736","title":"MANDERA: Malicious Node Detection in Federated Learning via Ranking","created":"2021-10-22","cited_by_count":0,"area":null},{"id":"2306.01342","title":"Covert Communication Based on the Poisoning Attack in Federated Learning","created":"2023-06-02","cited_by_count":0,"area":null},{"id":"2407.00719","title":"A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning","created":"2024-06-30","cited_by_count":0,"area":null},{"id":"2409.13875","title":"Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue","created":"2024-09-20","cited_by_count":0,"area":null},{"id":"2501.12123","title":"FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2502.05211","title":"Decoding FL Defenses: Systemization, Pitfalls, and Remedies","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2502.16396","title":"FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning","created":"2025-02-23","cited_by_count":0,"area":"safety"},{"id":"2503.10218","title":"Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models","created":"2025-03-13","cited_by_count":0,"area":null},{"id":"2503.20884","title":"Byzantine-Robust Federated Learning Using Generative Adversarial Networks","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2509.08089","title":"Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2509.20193","title":"FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2510.08311","title":"Robust and Efficient Collaborative Learning","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2512.12022","title":"DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2604.26116","title":"Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data","created":"2026-04-28","cited_by_count":0,"area":null},{"id":"2608.12108","title":"Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}