{"total":194,"rows":[{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"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":"2110.11794","title":"Federated Unlearning via Class-Discriminative Pruning","created":"2021-10-22","cited_by_count":115,"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":"2208.12268","title":"FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning","created":"2022-08-25","cited_by_count":91,"area":"safety"},{"id":"2201.09441","title":"Federated Unlearning with Knowledge Distillation","created":"2022-01-24","cited_by_count":73,"area":"safety"},{"id":"2505.02077","title":"Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents","created":"2025-05-04","cited_by_count":71,"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":"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":"2205.12709","title":"VeriFi: Towards Verifiable Federated Unlearning","created":"2022-05-25","cited_by_count":46,"area":"safety"},{"id":"2302.02069","title":"Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning","created":"2023-02-04","cited_by_count":40,"area":"safety"},{"id":"2109.13236","title":"FedIPR: Ownership Verification for Federated Deep Neural Network Models","created":"2021-09-27","cited_by_count":36,"area":"safety"},{"id":"2405.07406","title":"Machine Unlearning: A Comprehensive Survey","created":"2024-05-13","cited_by_count":35,"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":"2211.07160","title":"FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model","created":"2022-11-14","cited_by_count":30,"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":"2105.02866","title":"Membership Inference Attacks on Deep Regression Models for Neuroimaging","created":"2021-05-06","cited_by_count":22,"area":"safety"},{"id":"2210.16424","title":"Machine Unlearning of Federated Clusters","created":"2022-10-28","cited_by_count":22,"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":"2505.01976","title":"A Survey on Privacy Risks and Protection in Large Language Models","created":"2025-05-04","cited_by_count":21,"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":"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":"2102.05257","title":"Robust Federated Learning with Attack-Adaptive Aggregation","created":"2021-02-10","cited_by_count":17,"area":"safety"},{"id":"2302.12448","title":"Subspace based Federated Unlearning","created":"2023-02-24","cited_by_count":17,"area":"safety"},{"id":"2412.20200","title":"Federated Unlearning with Gradient Descent and Conflict Mitigation","created":"2024-12-28","cited_by_count":17,"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":"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":"2406.06186","title":"A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks","created":"2024-06-10","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":"2309.10283","title":"FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning","created":"2023-09-19","cited_by_count":13,"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":"2306.01356","title":"FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking","created":"2023-06-02","cited_by_count":12,"area":"safety"},{"id":"2402.06289","title":"FedMIA: An Effective Membership Inference Attack Exploiting \"All for One\" Principle in Federated Learning","created":"2024-02-09","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":"2311.15603","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":"2205.15970","title":"FedHarmony: Unlearning Scanner Bias with Distributed Data","created":"2022-05-31","cited_by_count":10,"area":"safety"},{"id":"2312.03205","title":"Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning","created":"2023-12-06","cited_by_count":10,"area":"safety"},{"id":"2407.02356","title":"Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging","created":"2024-07-02","cited_by_count":9,"area":"safety"},{"id":"2501.16663","title":"Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning","created":"2025-01-28","cited_by_count":9,"area":"safety"},{"id":"2102.05561","title":"Meta Federated Learning","created":"2021-02-10","cited_by_count":8,"area":"safety"},{"id":"2210.06894","title":"Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation","created":"2022-10-13","cited_by_count":8,"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":"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":"2407.20181","title":"Blockchain for Large Language Model Security and Safety: A Holistic Survey","created":"2024-07-26","cited_by_count":8,"area":"safety"},{"id":"2210.09305","title":"Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning","created":"2022-10-17","cited_by_count":7,"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":"2111.01363","title":"Knowledge Cross-Distillation for Membership Privacy","created":"2021-11-02","cited_by_count":6,"area":"safety"},{"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":"2403.17775","title":"Secure Aggregation is Not Private Against Membership Inference Attacks","created":"2024-03-26","cited_by_count":6,"area":"safety"},{"id":"2306.02216","title":"Forgettable Federated Linear Learning with Certified Data Unlearning","created":"2023-06-03","cited_by_count":5,"area":"safety"},{"id":"2401.01896","title":"Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification","created":"2023-10-22","cited_by_count":5,"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":"2411.19335","title":"PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning","created":"2024-11-28","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":"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":"2209.01994","title":"Federated Zero-Shot Learning for Visual Recognition","created":"2022-09-05","cited_by_count":4,"area":"safety"},{"id":"2303.11337","title":"Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning","created":"2023-03-20","cited_by_count":4,"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":"2308.13269","title":"Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation","created":"2023-08-25","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":"2402.19054","title":"RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection","created":"2024-02-29","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":"2603.12977","title":"Exact Federated Continual Unlearning for Ridge Heads on Frozen Foundation Models","created":"2026-03-13","cited_by_count":4,"area":"safety"},{"id":"2202.05469","title":"Privacy-preserving Generative Framework Against Membership Inference Attacks","created":"2022-02-11","cited_by_count":3,"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":"2308.00553","title":"FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning","created":"2023-08-01","cited_by_count":3,"area":"safety"},{"id":"2401.10375","title":"Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities","created":"2024-01-18","cited_by_count":3,"area":"safety"},{"id":"2403.06131","title":"PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples","created":"2024-03-10","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":"2411.11039","title":"FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method","created":"2024-11-17","cited_by_count":3,"area":"safety"},{"id":"2501.13683","title":"Unlearning Clients, Features and Samples in Vertical Federated Learning","created":"2025-01-23","cited_by_count":3,"area":"safety"},{"id":"2509.11080","title":"Membership Inference Attacks on Recommender System: A Survey","created":"2025-09-14","cited_by_count":3,"area":"safety"},{"id":"2511.11020","title":"Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis","created":"2025-11-14","cited_by_count":3,"area":"safety"},{"id":"2604.21197","title":"Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach","created":"2026-04-23","cited_by_count":3,"area":"safety"},{"id":"2210.11061","title":"Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario","created":"2022-10-20","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":"2406.03078","title":"Towards Federated Domain Unlearning: Verification Methodologies and Challenges","created":"2024-06-05","cited_by_count":2,"area":"safety"},{"id":"2407.19119","title":"Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning","created":"2024-07-26","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":"2410.09947","title":"Efficient Federated Unlearning under Plausible Deniability","created":"2024-10-13","cited_by_count":2,"area":"safety"},{"id":"2410.21453","title":"Inverting Gradient Attacks Makes Powerful Data Poisoning","created":"2024-10-28","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":"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":"2507.13591","title":"FuSeFL: Fully Secure and Scalable Federated Learning","created":"2025-07-18","cited_by_count":2,"area":"safety"},{"id":"2508.08875","title":"Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models","created":"2025-08-12","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":"2601.22589","title":"FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery","created":"2026-01-30","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":"2310.07380","title":"Histopathological Image Classification and Vulnerability Analysis using Federated Learning","created":"2023-10-11","cited_by_count":1,"area":"safety"}],"next_cursor":"100"}