{"total":125,"rows":[{"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":"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":"2406.10416","title":"Byzantine-Robust Decentralized Federated Learning","created":"2024-06-14","cited_by_count":48,"area":null},{"id":"2303.06818","title":"Backdoor Defense via Deconfounded Representation Learning","created":"2023-03-13","cited_by_count":38,"area":null},{"id":"2311.14975","title":"Eliminating Domain Bias for Federated Learning in Representation Space","created":"2023-11-25","cited_by_count":38,"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":"2210.00584","title":"FLCert: Provably Secure Federated Learning against Poisoning Attacks","created":"2022-10-02","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":"2308.04604","title":"A Survey on Decentralized Federated Learning","created":"2023-08-08","cited_by_count":27,"area":null},{"id":"2305.13997","title":"Learning Subpocket Prototypes for Generalizable Structure-based Drug Design","created":"2023-05-22","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":"2308.04466","title":"Backdoor Federated Learning by Poisoning Backdoor-Critical Layers","created":"2023-08-08","cited_by_count":18,"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":"2308.05832","title":"FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks","created":"2023-08-10","cited_by_count":15,"area":null},{"id":"2502.16184","title":"Robustness and Cybersecurity in the EU Artificial Intelligence Act","created":"2025-02-22","cited_by_count":11,"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":"2310.09789","title":"FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy","created":"2023-10-15","cited_by_count":10,"area":null},{"id":"2306.04984","title":"G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering","created":"2023-06-08","cited_by_count":9,"area":null},{"id":"2311.18498","title":"Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach","created":"2023-11-30","cited_by_count":9,"area":null},{"id":"2210.07376","title":"ScionFL: Efficient and Robust Secure Quantized Aggregation","created":"2022-10-13","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":"2502.08123","title":"Provably Robust Federated Reinforcement Learning","created":"2025-02-12","cited_by_count":8,"area":"deeprl"},{"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":"2509.20383","title":"MARS: A Malignity-Aware Backdoor Defense in Federated Learning","created":"2025-09-21","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":"2407.07221","title":"Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning","created":"2024-07-09","cited_by_count":6,"area":null},{"id":"2412.11476","title":"Vertical Federated Unlearning via Backdoor Certification","created":"2024-12-16","cited_by_count":6,"area":null},{"id":"2308.11333","title":"FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning","created":"2023-08-22","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},{"id":"2405.20975","title":"ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning","created":"2024-05-31","cited_by_count":5,"area":null},{"id":"2502.03340","title":"Interaction-Aware Gaussian Weighting for Clustered Federated Learning","created":"2025-02-05","cited_by_count":5,"area":null},{"id":"2307.14364","title":"Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis","created":"2023-07-25","cited_by_count":4,"area":null},{"id":"2310.08097","title":"Sentinel: An Aggregation Function to Secure Decentralized Federated Learning","created":"2023-10-12","cited_by_count":4,"area":null},{"id":"2312.12484","title":"SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks","created":"2023-12-19","cited_by_count":4,"area":null},{"id":"2405.11440","title":"A Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated Learning","created":"2024-05-19","cited_by_count":4,"area":"safety"},{"id":"2410.02042","title":"EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning","created":"2024-10-02","cited_by_count":4,"area":null},{"id":"2503.08976","title":"Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning","created":"2025-03-12","cited_by_count":4,"area":null},{"id":"2505.09110","title":"Toward Malicious Clients Detection in Federated Learning","created":"2025-05-14","cited_by_count":4,"area":null},{"id":"2301.05795","title":"Poisoning Attacks and Defenses in Federated Learning: A Survey","created":"2023-01-14","cited_by_count":3,"area":null},{"id":"2303.16668","title":"Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection","created":"2023-03-29","cited_by_count":3,"area":null},{"id":"2406.12222","title":"BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning","created":"2024-06-18","cited_by_count":3,"area":null},{"id":"2407.15402","title":"Tackling Selfish Clients in Federated Learning","created":"2024-07-22","cited_by_count":3,"area":null},{"id":"2409.17328","title":"The poison of dimensionality","created":"2024-09-25","cited_by_count":3,"area":null},{"id":"2505.07614","title":"Bant: Byzantine Antidote via Trial Function and Trust Scores","created":"2025-05-12","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":"2211.05739","title":"FedLesScan: Mitigating Stragglers in Serverless Federated Learning","created":"2022-11-10","cited_by_count":2,"area":null},{"id":"2302.09904","title":"WW-FL: Secure and Private Large-Scale Federated Learning","created":"2023-02-20","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":"2310.13862","title":"Competitive Advantage Attacks to Decentralized Federated Learning","created":"2023-10-20","cited_by_count":2,"area":null},{"id":"2405.13080","title":"EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection","created":"2024-05-21","cited_by_count":2,"area":null},{"id":"2407.09958","title":"Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning","created":"2024-07-13","cited_by_count":2,"area":null},{"id":"2407.15389","title":"Poisoning with A Pill: Circumventing Detection in Federated Learning","created":"2024-07-22","cited_by_count":2,"area":null},{"id":"2409.19912","title":"HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning","created":"2024-09-30","cited_by_count":2,"area":"distill"},{"id":"2411.02773","title":"FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2412.08763","title":"Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI","created":"2024-12-11","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":"2506.22506","title":"SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learning","created":"2025-06-25","cited_by_count":2,"area":"safety"},{"id":"2603.00516","title":"ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data","created":"2026-02-28","cited_by_count":2,"area":"safety"},{"id":"2306.03600","title":"Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations","created":"2023-06-06","cited_by_count":1,"area":"safety"},{"id":"2404.04139","title":"Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning","created":"2024-04-05","cited_by_count":1,"area":"safety"},{"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":"2408.02813","title":"Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense","created":"2024-08-05","cited_by_count":1,"area":"safety"},{"id":"2409.00614","title":"DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism","created":"2024-09-01","cited_by_count":1,"area":null},{"id":"2410.13083","title":"FedCAP: Robust Federated Learning via Customized Aggregation and Personalization","created":"2024-10-16","cited_by_count":1,"area":null},{"id":"2503.23288","title":"Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks","created":"2025-03-30","cited_by_count":1,"area":null},{"id":"2504.07101","title":"Personalized Recommendation Models in Federated Settings: A Survey","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2505.23792","title":"Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2507.04903","title":"BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2508.03579","title":"Heterogeneity-Oblivious Robust Federated Learning","created":"2025-08-05","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":"2509.18044","title":"Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments","created":"2025-09-22","cited_by_count":1,"area":"safety"},{"id":"2601.14687","title":"Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning","created":"2026-01-21","cited_by_count":1,"area":null},{"id":"2602.15161","title":"Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning","created":"2026-02-16","cited_by_count":1,"area":null},{"id":"2608.22552","title":"Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions","created":"2026-08-23","cited_by_count":1,"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":"2309.00127","title":"FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning","created":"2023-08-31","cited_by_count":0,"area":null},{"id":"2401.05569","title":"SENet: Visual Detection of Online Social Engineering Attack Campaigns","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2402.16934","title":"FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge","created":"2024-02-26","cited_by_count":0,"area":null},{"id":"2404.06230","title":"Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning","created":"2024-04-09","cited_by_count":0,"area":null},{"id":"2406.02605","title":"A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder","created":"2024-06-02","cited_by_count":0,"area":null},{"id":"2406.14217","title":"Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning","created":"2024-06-20","cited_by_count":0,"area":null},{"id":"2411.03231","title":"LOGSAFE: Logic-Guided Verification for Trustworthy Federated Time-Series Learning","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2411.03861","title":"FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning","created":"2024-11-06","cited_by_count":0,"area":null},{"id":"2411.03926","title":"Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learning","created":"2024-11-06","cited_by_count":0,"area":null},{"id":"2411.10673","title":"How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution","created":"2024-11-16","cited_by_count":0,"area":null},{"id":"2501.02971","title":"Proof-of-Data: A Consensus Protocol for Collaborative Intelligence","created":"2025-01-06","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":"2503.13537","title":"FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning","created":"2025-03-15","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":"2503.23511","title":"Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering","created":"2025-03-30","cited_by_count":0,"area":null},{"id":"2505.14024","title":"FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2507.15104","title":"AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI","created":"2025-07-20","cited_by_count":0,"area":null},{"id":"2507.21177","title":"FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning","created":"2025-07-26","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":"2509.10503","title":"FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free","created":"2025-09-01","cited_by_count":0,"area":null},{"id":"2509.11974","title":"Poison to Detect: Detection of Targeted Overfitting in Federated Learning","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2509.18904","title":"Enhancing the Effectiveness and Durability of Backdoor Attacks in Federated Learning through Maximizing Task Distinction","created":"2025-09-23","cited_by_count":0,"area":null}],"next_cursor":"100"}