{"total":22,"rows":[{"id":"2203.07320","title":"The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining","created":"2022-03-14","cited_by_count":118,"area":"safety"},{"id":"2404.03233","title":"Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning","created":"2024-04-04","cited_by_count":48,"area":"safety"},{"id":"2302.07225","title":"Bounding Training Data Reconstruction in DP-SGD","created":"2023-02-14","cited_by_count":46,"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":"2206.03317","title":"Subject Membership Inference Attacks in Federated Learning","created":"2022-06-07","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":"2401.11018","title":"Communication Efficient and Provable Federated Unlearning","created":"2024-01-19","cited_by_count":18,"area":"safety"},{"id":"2111.00430","title":"Efficient passive membership inference attack in federated learning","created":"2021-10-31","cited_by_count":16,"area":"safety"},{"id":"2403.02116","title":"Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks","created":"2024-03-04","cited_by_count":13,"area":"safety"},{"id":"2306.01356","title":"FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking","created":"2023-06-02","cited_by_count":12,"area":"safety"},{"id":"2310.00222","title":"Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning","created":"2023-09-30","cited_by_count":11,"area":"safety"},{"id":"2311.14056","title":"DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release","created":"2023-11-23","cited_by_count":10,"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":"2203.16463","title":"Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning","created":"2022-03-30","cited_by_count":3,"area":"safety"},{"id":"2407.00866","title":"Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning","created":"2024-07-01","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":"2306.03600","title":"Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations","created":"2023-06-06","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":"2405.15423","title":"Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models","created":"2024-05-24","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":"2506.01777","title":"DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems","created":"2025-06-02","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"}],"next_cursor":null}