{"total":28,"rows":[{"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":"2412.06157","title":"Membership Inference Attacks and Defenses in Federated Learning: A Survey","created":"2024-12-09","cited_by_count":31,"area":"safety"},{"id":"2102.09171","title":"Data Poisoning Attacks and Defenses to Crowdsourcing Systems","created":"2021-02-18","cited_by_count":12,"area":"safety"},{"id":"2210.10886","title":"Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis","created":"2022-10-19","cited_by_count":12,"area":"safety"},{"id":"2304.00160","title":"Secure Federated Learning against Model Poisoning Attacks via Client Filtering","created":"2023-03-31","cited_by_count":8,"area":"safety"},{"id":"2404.09724","title":"Privacy-Preserving Federated Unlearning with Certified Client Removal","created":"2024-04-15","cited_by_count":5,"area":"safety"},{"id":"2501.17396","title":"Poisoning Attacks and Defenses to Federated Unlearning","created":"2025-01-29","cited_by_count":5,"area":"safety"},{"id":"2303.03648","title":"Can Membership Inferencing be Refuted?","created":"2023-03-07","cited_by_count":4,"area":"safety"},{"id":"2405.15316","title":"Decaf: Data Distribution Decompose Attack against Federated Learning","created":"2024-05-24","cited_by_count":4,"area":"safety"},{"id":"2411.11044","title":"Efficient Federated Unlearning with Adaptive Differential Privacy Preservation","created":"2024-11-17","cited_by_count":4,"area":"safety"},{"id":"2211.14769","title":"Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning","created":"2022-11-27","cited_by_count":2,"area":"safety"},{"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":"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":"2311.05826","title":"Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios","created":"2023-11-10","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":"2409.13004","title":"Data Poisoning and Leakage Analysis in Federated Learning","created":"2024-09-19","cited_by_count":1,"area":"safety"},{"id":"2502.07011","title":"DROP: Poison Dilution via Knowledge Distillation for Federated Learning","created":"2025-02-10","cited_by_count":1,"area":"safety"},{"id":"2507.00423","title":"Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning","created":"2025-07-01","cited_by_count":1,"area":"safety"},{"id":"2508.10315","title":"A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning","created":"2025-08-14","cited_by_count":1,"area":"safety"},{"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":"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":"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":"2602.05612","title":"ADCA: Attention-Driven Multi-Party Collusion Attack in Federated Self-Supervised Learning","created":"2026-02-05","cited_by_count":0,"area":"safety"},{"id":"2605.02110","title":"Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery","created":"2026-05-04","cited_by_count":0,"area":"safety"},{"id":"2606.31066","title":"Secure-CHG: A Comprehensive Framework for Robust and Fair Federated Learning via Hybrid Defense and Contribution-Aware Trust","created":"2026-06-30","cited_by_count":0,"area":"safety"},{"id":"2610.18430","title":"FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation","created":"2026-10-13","cited_by_count":0,"area":"safety"}],"next_cursor":null}