{"total":42,"rows":[{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"area":"safety"},{"id":"2308.07061","title":"Machine Unlearning: Solutions and Challenges","created":"2023-08-14","cited_by_count":93,"area":"safety"},{"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":"2305.06360","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","created":"2023-05-10","cited_by_count":51,"area":"safety"},{"id":"2401.05146","title":"Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics","created":"2024-01-10","cited_by_count":27,"area":"safety"},{"id":"2210.09126","title":"Verifiable and Provably Secure Machine Unlearning","created":"2022-10-17","cited_by_count":25,"area":"safety"},{"id":"2502.12176","title":"Ten Challenging Problems in Federated Foundation Models","created":"2025-02-14","cited_by_count":20,"area":"safety"},{"id":"2210.11334","title":"Proof of Unlearning: Definitions and Instantiation","created":"2022-10-20","cited_by_count":15,"area":"safety"},{"id":"2503.05657","title":"NoT: Federated Unlearning via Weight Negation","created":"2025-03-07","cited_by_count":15,"area":"safety"},{"id":"2405.15474","title":"Unlearning during Learning: An Efficient Federated Machine Unlearning Method","created":"2024-05-24","cited_by_count":14,"area":"safety"},{"id":"2311.15603","title":"QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation","created":"2023-11-27","cited_by_count":11,"area":"safety"},{"id":"2405.17462","title":"Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity","created":"2024-05-23","cited_by_count":11,"area":"safety"},{"id":"2402.01276","title":"Federated Unlearning: a Perspective of Stability and Fairness","created":"2024-02-02","cited_by_count":7,"area":"safety"},{"id":"2401.15917","title":"Blockchain-enabled Trustworthy Federated Unlearning","created":"2024-01-29","cited_by_count":6,"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":"2410.10922","title":"Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure","created":"2024-10-14","cited_by_count":5,"area":"safety"},{"id":"2605.00733","title":"EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure","created":"2026-05-01","cited_by_count":5,"area":"safety"},{"id":"2311.13174","title":"SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning","created":"2023-11-22","cited_by_count":4,"area":"safety"},{"id":"2401.02457","title":"eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning","created":"2024-01-04","cited_by_count":4,"area":"safety"},{"id":"2401.13410","title":"How to Forget Clients in Federated Online Learning to Rank?","created":"2024-01-24","cited_by_count":4,"area":"safety"},{"id":"2410.04144","title":"ConDa: Fast Federated Unlearning with Contribution Dampening","created":"2024-10-05","cited_by_count":2,"area":"safety"},{"id":"2502.16708","title":"Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions","created":"2025-02-23","cited_by_count":2,"area":"safety"},{"id":"2512.09953","title":"ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs","created":"2025-12-09","cited_by_count":2,"area":"safety"},{"id":"2408.08493","title":"Parallel Unlearning in Inherited Model Networks","created":"2024-08-16","cited_by_count":1,"area":"safety"},{"id":"2411.17126","title":"From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models","created":"2024-11-26","cited_by_count":1,"area":"safety"},{"id":"2504.14798","title":"RUB: Evaluating Residual Knowledge in Unlearned Models","created":"2025-04-21","cited_by_count":1,"area":"safety"},{"id":"2508.07873","title":"EFU: Enforcing Federated Unlearning via Functional Encryption","created":"2025-08-11","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":"2509.15861","title":"ToFU: Transforming How Federated Learning Systems Forget User Data","created":"2025-09-19","cited_by_count":1,"area":"safety"},{"id":"2509.25613","title":"SMS: Self-supervised Model Seeding for Verification of Machine Unlearning","created":"2025-09-30","cited_by_count":1,"area":"safety"},{"id":"2604.12348","title":"PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning","created":"2026-04-14","cited_by_count":1,"area":"safety"},{"id":"2403.02437","title":"A Survey on Federated Unlearning: Challenges and Opportunities","created":"2024-03-04","cited_by_count":0,"area":"safety"},{"id":"2412.00126","title":"Streamlined Federated Unlearning: Unite as One to Be Highly Efficient","created":"2024-11-28","cited_by_count":0,"area":"safety"},{"id":"2507.09602","title":"DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences","created":"2025-07-13","cited_by_count":0,"area":"safety"},{"id":"2510.00833","title":"Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead","created":"2025-10-01","cited_by_count":0,"area":"safety"},{"id":"2512.13381","title":"Dual-Phase Federated Deep Unlearning via Weight-Aware Rollback and Reconstruction","created":"2025-12-15","cited_by_count":0,"area":"safety"},{"id":"2603.29688","title":"Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks","created":"2026-03-31","cited_by_count":0,"area":"safety"},{"id":"2604.04800","title":"Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation","created":"2026-04-06","cited_by_count":0,"area":"safety"},{"id":"2606.14518","title":"Characterizing Privacy-Audit Alignment in Behavioral Audit of Machine Unlearning","created":"2026-06-12","cited_by_count":0,"area":"safety"},{"id":"2606.16304","title":"pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning","created":"2026-06-15","cited_by_count":0,"area":"safety"},{"id":"2607.07635","title":"Unlearning to Protect: A Distilled Reinforcement Learning Framework with Privacy-Preserving Feature Unlearning and XAI for IoT Security","created":"2026-07-08","cited_by_count":0,"area":"safety"},{"id":"2607.28829","title":"When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks","created":"2026-07-30","cited_by_count":0,"area":"safety"}],"next_cursor":null}