{"total":118,"rows":[{"id":"2310.12508","title":"SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation","created":"2023-10-19","cited_by_count":386,"area":"diffusion"},{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"area":"safety"},{"id":"2304.04934","title":"Model Sparsity Can Simplify Machine Unlearning","created":"2023-04-11","cited_by_count":214,"area":"safety"},{"id":"2303.11570","title":"Boundary Unlearning","created":"2023-03-21","cited_by_count":134,"area":"safety"},{"id":"2207.05521","title":"Federated Unlearning: How to Efficiently Erase a Client in FL?","created":"2022-07-12","cited_by_count":95,"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":"2401.06187","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","created":"2024-01-11","cited_by_count":52,"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":"2406.12038","title":"Soft Prompting for Unlearning in Large Language Models","created":"2024-06-17","cited_by_count":46,"area":"safety"},{"id":"2403.07362","title":"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning","created":"2024-03-12","cited_by_count":44,"area":"safety"},{"id":"2302.13406","title":"GNNDelete: A General Strategy for Unlearning in Graph Neural Networks","created":"2023-02-26","cited_by_count":43,"area":null},{"id":"2403.13682","title":"Threats, Attacks, and Defenses in Machine Unlearning: A Survey","created":"2024-03-20","cited_by_count":39,"area":"safety"},{"id":"2404.15381","title":"Advances and Open Challenges in Federated Foundation Models","created":"2024-04-23","cited_by_count":36,"area":null},{"id":"2405.07406","title":"Machine Unlearning: A Comprehensive Survey","created":"2024-05-13","cited_by_count":35,"area":"safety"},{"id":"2210.08196","title":"Deep Regression Unlearning","created":"2022-10-15","cited_by_count":32,"area":"safety"},{"id":"2304.13169","title":"SAFE: Machine Unlearning With Shard Graphs","created":"2023-04-25","cited_by_count":28,"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":"2410.17509","title":"WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models","created":"2024-10-23","cited_by_count":26,"area":"safety"},{"id":"2311.12047","title":"MultiDelete for Multimodal Machine Unlearning","created":"2023-11-18","cited_by_count":20,"area":"safety"},{"id":"2502.12176","title":"Ten Challenging Problems in Federated Foundation Models","created":"2025-02-14","cited_by_count":20,"area":"safety"},{"id":"2306.03558","title":"Machine Unlearning: A Survey","created":"2023-06-06","cited_by_count":19,"area":"safety"},{"id":"2503.06339","title":"Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning","created":"2025-03-08","cited_by_count":19,"area":"safety"},{"id":"2211.11656","title":"SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization","created":"2022-11-21","cited_by_count":18,"area":"safety"},{"id":"2401.11018","title":"Communication Efficient and Provable Federated Unlearning","created":"2024-01-19","cited_by_count":18,"area":"safety"},{"id":"2406.14796","title":"MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning","created":"2024-06-21","cited_by_count":17,"area":"safety"},{"id":"2411.15537","title":"MUNBa: Machine Unlearning via Nash Bargaining","created":"2024-11-23","cited_by_count":17,"area":"safety"},{"id":"2502.20709","title":"Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter","created":"2025-02-28","cited_by_count":16,"area":"safety"},{"id":"2210.11334","title":"Proof of Unlearning: Definitions and Instantiation","created":"2022-10-20","cited_by_count":15,"area":"safety"},{"id":"2412.06966","title":"Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research","created":"2024-12-09","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":"2304.10638","title":"Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning","created":"2023-04-20","cited_by_count":13,"area":null},{"id":"2405.17462","title":"Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity","created":"2024-05-23","cited_by_count":11,"area":"safety"},{"id":"2404.06114","title":"Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey","created":"2024-04-09","cited_by_count":10,"area":null},{"id":"2408.04140","title":"UNLEARN Efficient Removal of Knowledge in Large Language Models","created":"2024-08-08","cited_by_count":10,"area":null},{"id":"2406.07698","title":"Label Smoothing Improves Machine Unlearning","created":"2024-06-11","cited_by_count":9,"area":"safety"},{"id":"2504.02199","title":"ESC: Erasing Space Concept for Knowledge Deletion","created":"2025-04-03","cited_by_count":9,"area":null},{"id":"2405.18040","title":"Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience","created":"2024-05-28","cited_by_count":8,"area":"safety"},{"id":"2506.15115","title":"Towards Reliable Forgetting: A Survey on Machine Unlearning Verification","created":"2025-06-18","cited_by_count":8,"area":"safety"},{"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":"2402.01276","title":"Federated Unlearning: a Perspective of Stability and Fairness","created":"2024-02-02","cited_by_count":7,"area":"safety"},{"id":"2503.02312","title":"Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization","created":"2025-03-04","cited_by_count":7,"area":"safety"},{"id":"2206.04742","title":"Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying","created":"2022-06-09","cited_by_count":6,"area":null},{"id":"2307.03363","title":"Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation","created":"2023-07-07","cited_by_count":6,"area":"safety"},{"id":"2401.15917","title":"Blockchain-enabled Trustworthy Federated Unlearning","created":"2024-01-29","cited_by_count":6,"area":"safety"},{"id":"2401.15957","title":"Scalable Federated Unlearning via Isolated and Coded Sharding","created":"2024-01-29","cited_by_count":6,"area":"safety"},{"id":"2508.21300","title":"Improving Fisher Information Estimation and Efficiency for LoRA-based LLM Unlearning","created":"2025-08-29","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":"2406.10953","title":"Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs","created":"2024-06-16","cited_by_count":5,"area":"safety"},{"id":"2406.12516","title":"Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation","created":"2024-06-18","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":"2510.22124","title":"Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery","created":"2025-10-25","cited_by_count":5,"area":"safety"},{"id":"2306.01431","title":"On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2308.10422","title":"Split Unlearning","created":"2023-08-21","cited_by_count":4,"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":"2401.13410","title":"How to Forget Clients in Federated Online Learning to Rank?","created":"2024-01-24","cited_by_count":4,"area":"safety"},{"id":"2405.20776","title":"Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach","created":"2024-05-27","cited_by_count":4,"area":"safety"},{"id":"2402.16294","title":"BlockFUL: Enabling Unlearning in Blockchained Federated Learning","created":"2024-02-26","cited_by_count":3,"area":"safety"},{"id":"2404.03180","title":"Goldfish: An Efficient Federated Unlearning Framework","created":"2024-04-04","cited_by_count":3,"area":"safety"},{"id":"2410.10120","title":"Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications","created":"2024-10-14","cited_by_count":3,"area":"safety"},{"id":"2502.15839","title":"FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities","created":"2025-02-20","cited_by_count":3,"area":null},{"id":"2505.12791","title":"Unlearning for Federated Online Learning to Rank: A Reproducibility Study","created":"2025-05-19","cited_by_count":3,"area":null},{"id":"2511.13116","title":"Synthetic Forgetting without Access: A Few-shot Zero-glance Framework for Machine Unlearning","created":"2025-11-17","cited_by_count":3,"area":"safety"},{"id":"2401.04385","title":"Machine unlearning through fine-grained model parameters perturbation","created":"2024-01-09","cited_by_count":2,"area":"safety"},{"id":"2402.09710","title":"Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks","created":"2024-02-15","cited_by_count":2,"area":null},{"id":"2404.11056","title":"LMEraser: Large Model Unlearning through Adaptive Prompt Tuning","created":"2024-04-17","cited_by_count":2,"area":"safety"},{"id":"2406.03078","title":"Towards Federated Domain Unlearning: Verification Methodologies and Challenges","created":"2024-06-05","cited_by_count":2,"area":"safety"},{"id":"2408.07587","title":"FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher","created":"2024-08-14","cited_by_count":2,"area":"safety"},{"id":"2410.04144","title":"ConDa: Fast Federated Unlearning with Contribution Dampening","created":"2024-10-05","cited_by_count":2,"area":"safety"},{"id":"2410.09947","title":"Efficient Federated Unlearning under Plausible Deniability","created":"2024-10-13","cited_by_count":2,"area":"safety"},{"id":"2502.14558","title":"Model Inversion Attack against Federated Unlearning","created":"2025-02-20","cited_by_count":2,"area":"safety"},{"id":"2510.17895","title":"Hierarchical Federated Unlearning for Large Language Models","created":"2025-10-19","cited_by_count":2,"area":"safety"},{"id":"2601.06436","title":"Certified Unlearning in Decentralized Federated Learning","created":"2026-01-10","cited_by_count":2,"area":"safety"},{"id":"2402.03770","title":"Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes","created":"2024-02-06","cited_by_count":1,"area":null},{"id":"2406.10954","title":"Towards Efficient Target-Level Machine Unlearning Based on Essential Graph","created":"2024-06-16","cited_by_count":1,"area":"safety"},{"id":"2407.05915","title":"Harnessing Federated Generative Learning for Green and Sustainable Internet of Things","created":"2024-04-30","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":"2504.05822","title":"Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients","created":"2025-04-08","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":"2507.23257","title":"Efficient Machine Unlearning via Influence Approximation","created":"2025-07-31","cited_by_count":1,"area":"safety"},{"id":"2508.01620","title":"IMU: Influence-guided Machine Unlearning","created":"2025-08-03","cited_by_count":1,"area":"safety"},{"id":"2508.15541","title":"BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning","created":"2025-08-21","cited_by_count":1,"area":null},{"id":"2509.15861","title":"ToFU: Transforming How Federated Learning Systems Forget User Data","created":"2025-09-19","cited_by_count":1,"area":"safety"},{"id":"2511.03966","title":"PrivacyCD: Hierarchical Unlearning for Protecting Student Privacy in Cognitive Diagnosis","created":"2025-11-06","cited_by_count":1,"area":"safety"},{"id":"2511.04716","title":"P-MIA: A Profiled-Based Membership Inference Attack on Cognitive Diagnosis Models","created":"2025-11-06","cited_by_count":1,"area":"safety"},{"id":"2511.08207","title":"FedPoP: Federated Learning Meets Proof of Participation","created":"2025-11-11","cited_by_count":1,"area":null},{"id":"2512.07374","title":"Recover-to-Forget: Gradient Reconstruction from LoRA for Efficient LLM Unlearning","created":"2025-12-08","cited_by_count":1,"area":"safety"},{"id":"2512.10348","title":"REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature","created":"2025-12-11","cited_by_count":1,"area":"safety"},{"id":"2601.07214","title":"BlindU: Blind Machine Unlearning without Revealing Erasing Data","created":"2026-01-12","cited_by_count":1,"area":"safety"},{"id":"2602.01150","title":"SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing","created":"2026-02-01","cited_by_count":1,"area":"safety"},{"id":"2604.04030","title":"Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement","created":"2026-04-05","cited_by_count":1,"area":"safety"},{"id":"2605.24545","title":"Rethinking Federated Unlearning via the Lens of Memorization","created":"2026-05-23","cited_by_count":1,"area":"safety"},{"id":"2607.21111","title":"TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning","created":"2026-07-23","cited_by_count":1,"area":"offlinerl"},{"id":"2403.02437","title":"A Survey on Federated Unlearning: Challenges and Opportunities","created":"2024-03-04","cited_by_count":0,"area":"safety"},{"id":"2410.06848","title":"Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation","created":"2024-10-09","cited_by_count":0,"area":"safety"},{"id":"2410.15045","title":"Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives","created":"2024-10-19","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":"2412.13466","title":"Federated Unlearning Model Recovery in Data with Skewed Label Distributions","created":"2024-12-18","cited_by_count":0,"area":null},{"id":"2504.01705","title":"Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning","created":"2025-04-02","cited_by_count":0,"area":"safety"},{"id":"2505.06520","title":"PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks","created":"2025-05-10","cited_by_count":0,"area":"safety"}],"next_cursor":"100"}