{"total":47,"rows":[{"id":"2205.12709","title":"VeriFi: Towards Verifiable Federated Unlearning","created":"2022-05-25","cited_by_count":46,"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":"2412.06157","title":"Membership Inference Attacks and Defenses in Federated Learning: A Survey","created":"2024-12-09","cited_by_count":31,"area":"safety"},{"id":"2502.12176","title":"Ten Challenging Problems in Federated Foundation Models","created":"2025-02-14","cited_by_count":20,"area":"safety"},{"id":"2102.05257","title":"Robust Federated Learning with Attack-Adaptive Aggregation","created":"2021-02-10","cited_by_count":17,"area":"safety"},{"id":"2202.08578","title":"An Equivalence Between Data Poisoning and Byzantine Gradient Attacks","created":"2022-02-17","cited_by_count":16,"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":"2305.05355","title":"Turning Privacy-preserving Mechanisms against Federated Learning","created":"2023-05-09","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":"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":"2411.11044","title":"Efficient Federated Unlearning with Adaptive Differential Privacy Preservation","created":"2024-11-17","cited_by_count":4,"area":"safety"},{"id":"2404.08631","title":"FCert: Certifiably Robust Few-Shot Classification in the Era of Foundation Models","created":"2024-04-12","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":"2211.14769","title":"Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning","created":"2022-11-27","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":"2405.00491","title":"On the Relevance of Byzantine Robust Optimization Against Data Poisoning","created":"2024-05-01","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":"2506.18020","title":"Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning","created":"2025-06-22","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":"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":"2311.05826","title":"Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios","created":"2023-11-10","cited_by_count":1,"area":"safety"},{"id":"2311.12715","title":"Attacks on fairness in Federated Learning","created":"2023-11-21","cited_by_count":1,"area":"safety"},{"id":"2404.04139","title":"Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning","created":"2024-04-05","cited_by_count":1,"area":"safety"},{"id":"2408.02813","title":"Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense","created":"2024-08-05","cited_by_count":1,"area":"safety"},{"id":"2502.07011","title":"DROP: Poison Dilution via Knowledge Distillation for Federated Learning","created":"2025-02-10","cited_by_count":1,"area":"safety"},{"id":"2504.03173","title":"PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks","created":"2025-04-04","cited_by_count":1,"area":"safety"},{"id":"2507.00423","title":"Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning","created":"2025-07-01","cited_by_count":1,"area":"safety"},{"id":"2508.10315","title":"A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning","created":"2025-08-14","cited_by_count":1,"area":"safety"},{"id":"2509.18044","title":"Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments","created":"2025-09-22","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":"2104.08020","title":"FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning","created":"2021-04-16","cited_by_count":0,"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":"2409.15067","title":"SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks","created":"2024-09-23","cited_by_count":0,"area":"safety"},{"id":"2501.04453","title":"Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning","created":"2025-01-08","cited_by_count":0,"area":"safety"},{"id":"2502.16396","title":"FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning","created":"2025-02-23","cited_by_count":0,"area":"safety"},{"id":"2508.02136","title":"FedLAD: A Linear Algebra Based Data Poisoning Defence for Federated Learning","created":"2025-08-04","cited_by_count":0,"area":"safety"},{"id":"2601.00900","title":"Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition","created":"2025-12-31","cited_by_count":0,"area":"safety"},{"id":"2603.18538","title":"Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning","created":"2026-03-19","cited_by_count":0,"area":"safety"},{"id":"2605.15885","title":"FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection","created":"2026-05-15","cited_by_count":0,"area":"safety"},{"id":"2606.12679","title":"Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning","created":"2026-06-10","cited_by_count":0,"area":"safety"},{"id":"2606.31066","title":"Secure-CHG: A Comprehensive Framework for Robust and Fair Federated Learning via Hybrid Defense and Contribution-Aware Trust","created":"2026-06-30","cited_by_count":0,"area":"safety"},{"id":"2608.04045","title":"Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity","created":"2026-08-04","cited_by_count":0,"area":"safety"},{"id":"2608.14861","title":"STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation","created":"2026-08-14","cited_by_count":0,"area":"safety"}],"next_cursor":null}