{"total":28,"rows":[{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"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":"2102.00655","title":"Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning","created":"2021-02-01","cited_by_count":21,"area":"safety"},{"id":"2302.12448","title":"Subspace based Federated Unlearning","created":"2023-02-24","cited_by_count":17,"area":"safety"},{"id":"2309.10283","title":"FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning","created":"2023-09-19","cited_by_count":13,"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":"2104.03834","title":"Bayesian Variational Federated Learning and Unlearning in Decentralized Networks","created":"2021-04-08","cited_by_count":9,"area":"safety"},{"id":"2501.18416","title":"Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation","created":"2025-01-30","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":"2307.03197","title":"Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks","created":"2023-07-04","cited_by_count":4,"area":"safety"},{"id":"2411.11039","title":"FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method","created":"2024-11-17","cited_by_count":3,"area":"safety"},{"id":"2207.13572","title":"Membership Inference Attacks via Adversarial Examples","created":"2022-07-27","cited_by_count":2,"area":"safety"},{"id":"2310.08015","title":"Why Train More? Effective and Efficient Membership Inference via Memorization","created":"2023-10-12","cited_by_count":2,"area":"safety"},{"id":"2312.04587","title":"FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks","created":"2023-12-04","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":"2408.01040","title":"Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix","created":"2024-08-02","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":"2412.12837","title":"Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing","created":"2024-12-17","cited_by_count":1,"area":"safety"},{"id":"2505.07188","title":"Securing Genomic Data Against Inference Attacks in Federated Learning Environments","created":"2025-05-12","cited_by_count":1,"area":"safety"},{"id":"2512.23171","title":"Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning","created":"2025-12-29","cited_by_count":1,"area":"safety"},{"id":"2601.06866","title":"United We Defend: Collaborative Membership Inference Defenses in Federated Learning","created":"2026-01-11","cited_by_count":1,"area":"safety"},{"id":"2209.06397","title":"Federated Learning based on Defending Against Data Poisoning Attacks in IoT","created":"2022-09-14","cited_by_count":0,"area":"safety"},{"id":"2509.25555","title":"Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches","created":"2025-09-29","cited_by_count":0,"area":"safety"},{"id":"2511.23393","title":"FedSGT: Exact Federated Unlearning via Sequential Group-based Training","created":"2025-11-28","cited_by_count":0,"area":"safety"},{"id":"2604.26809","title":"Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging","created":"2026-04-29","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":"2605.22871","title":"Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity","created":"2026-05-20","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"}],"next_cursor":null}