{"total":275,"rows":[{"id":"2111.08834","title":"Federated Learning for Smart Healthcare: A Survey","created":"2021-11-16","cited_by_count":184,"area":null},{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2108.10241","title":"Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning","created":"2021-08-23","cited_by_count":118,"area":null},{"id":"2205.01992","title":"Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning","created":"2022-05-04","cited_by_count":94,"area":"safety"},{"id":"2409.18169","title":"Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey","created":"2024-09-26","cited_by_count":90,"area":"alignment"},{"id":"2306.01603","title":"Decentralized Federated Learning: A Survey and Perspective","created":"2023-06-02","cited_by_count":86,"area":null},{"id":"2101.05428","title":"Federated Learning: Opportunities and Challenges","created":"2021-01-14","cited_by_count":84,"area":null},{"id":"2203.08669","title":"MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients","created":"2022-03-16","cited_by_count":68,"area":null},{"id":"2104.14362","title":"From Distributed Machine Learning to Federated Learning: A Survey","created":"2021-04-29","cited_by_count":66,"area":null},{"id":"2101.07511","title":"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge","created":"2021-01-19","cited_by_count":56,"area":null},{"id":"2502.08586","title":"Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks","created":"2025-02-12","cited_by_count":49,"area":"agents"},{"id":"2406.10416","title":"Byzantine-Robust Decentralized Federated Learning","created":"2024-06-14","cited_by_count":48,"area":null},{"id":"2301.01299","title":"Recent Advances on Federated Learning: A Systematic Survey","created":"2023-01-03","cited_by_count":41,"area":null},{"id":"2210.12873","title":"FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning","created":"2022-10-23","cited_by_count":37,"area":null},{"id":"2102.12920","title":"Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning","created":"2021-02-25","cited_by_count":35,"area":null},{"id":"2211.00463","title":"Amplifying Membership Exposure via Data Poisoning","created":"2022-11-01","cited_by_count":34,"area":"safety"},{"id":"2303.02213","title":"Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions","created":"2023-03-03","cited_by_count":33,"area":null},{"id":"2201.02873","title":"LoMar: A Local Defense Against Poisoning Attack on Federated Learning","created":"2022-01-08","cited_by_count":31,"area":null},{"id":"2307.08925","title":"Integration of Large Language Models and Federated Learning","created":"2023-07-18","cited_by_count":31,"area":null},{"id":"2210.00584","title":"FLCert: Provably Secure Federated Learning against Poisoning Attacks","created":"2022-10-02","cited_by_count":30,"area":null},{"id":"2510.23883","title":"Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges","created":"2025-10-27","cited_by_count":28,"area":"agents"},{"id":"2107.03311","title":"RoFL: Robustness of Secure Federated Learning","created":"2021-07-07","cited_by_count":25,"area":null},{"id":"2112.06872","title":"Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors","created":"2021-12-13","cited_by_count":25,"area":null},{"id":"2304.01731","title":"Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher","created":"2023-04-04","cited_by_count":25,"area":"distill"},{"id":"2302.10637","title":"A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy","created":"2023-02-21","cited_by_count":24,"area":null},{"id":"2306.04959","title":"FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs","created":"2023-06-08","cited_by_count":21,"area":null},{"id":"2106.08882","title":"Robust Training in High Dimensions via Block Coordinate Geometric Median Descent","created":"2021-06-16","cited_by_count":20,"area":null},{"id":"2502.12176","title":"Ten Challenging Problems in Federated Foundation Models","created":"2025-02-14","cited_by_count":20,"area":"safety"},{"id":"2503.22759","title":"Data Poisoning in Deep Learning: A Survey","created":"2025-03-27","cited_by_count":20,"area":"safety"},{"id":"2409.11585","title":"Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework","created":"2024-09-17","cited_by_count":19,"area":null},{"id":"2105.01798","title":"Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence","created":"2021-05-04","cited_by_count":18,"area":null},{"id":"2312.08667","title":"Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey","created":"2023-12-14","cited_by_count":18,"area":null},{"id":"2406.10630","title":"Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models","created":"2024-06-15","cited_by_count":18,"area":"alignment"},{"id":"2210.06516","title":"How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?","created":"2022-10-12","cited_by_count":17,"area":"safety"},{"id":"2405.19524","title":"AI Risk Management Should Incorporate Both Safety and Security","created":"2024-05-29","cited_by_count":17,"area":null},{"id":"2207.01982","title":"Defending against the Label-flipping Attack in Federated Learning","created":"2022-07-05","cited_by_count":16,"area":null},{"id":"2302.01677","title":"Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks","created":"2023-02-03","cited_by_count":16,"area":null},{"id":"2305.05506","title":"FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation","created":"2023-05-09","cited_by_count":16,"area":null},{"id":"2108.03980","title":"Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness","created":"2021-07-30","cited_by_count":15,"area":null},{"id":"2108.08770","title":"Learning-to-learn non-convex piecewise-Lipschitz functions","created":"2021-08-19","cited_by_count":15,"area":null},{"id":"2207.00872","title":"FL-Defender: Combating Targeted Attacks in Federated Learning","created":"2022-07-02","cited_by_count":15,"area":null},{"id":"2208.10134","title":"Machine Learning with Confidential Computing: A Systematization of Knowledge","created":"2022-08-22","cited_by_count":15,"area":null},{"id":"2308.05832","title":"FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks","created":"2023-08-10","cited_by_count":15,"area":null},{"id":"2407.06754","title":"Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges","created":"2024-07-09","cited_by_count":15,"area":null},{"id":"2409.01435","title":"Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation","created":"2024-09-02","cited_by_count":15,"area":null},{"id":"2501.17381","title":"Do We Really Need to Design New Byzantine-robust Aggregation Rules?","created":"2025-01-29","cited_by_count":15,"area":null},{"id":"2111.04394","title":"Get a Model! Model Hijacking Attack Against Machine Learning Models","created":"2021-11-08","cited_by_count":14,"area":null},{"id":"2302.11051","title":"Fusion of Global and Local Knowledge for Personalized Federated Learning","created":"2023-02-21","cited_by_count":14,"area":null},{"id":"2308.12857","title":"Fast Adversarial Training with Smooth Convergence","created":"2023-08-24","cited_by_count":14,"area":null},{"id":"2402.01096","title":"Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance","created":"2024-02-02","cited_by_count":14,"area":null},{"id":"2101.10904","title":"Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation","created":"2021-01-24","cited_by_count":13,"area":null},{"id":"2106.11257","title":"Secure Distributed Training at Scale","created":"2021-06-21","cited_by_count":12,"area":null},{"id":"2205.01423","title":"Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration","created":"2022-05-03","cited_by_count":12,"area":null},{"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":"2211.14952","title":"Federated Learning Attacks and Defenses: A Survey","created":"2022-11-27","cited_by_count":12,"area":null},{"id":"2405.09839","title":"Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations","created":"2024-05-16","cited_by_count":12,"area":null},{"id":"2110.07567","title":"Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis","created":"2021-10-14","cited_by_count":10,"area":null},{"id":"2204.03500","title":"Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation","created":"2022-04-07","cited_by_count":10,"area":null},{"id":"2304.10783","title":"Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning","created":"2023-04-21","cited_by_count":10,"area":null},{"id":"2305.03888","title":"Energy-Latency Attacks to On-Device Neural Networks via Sponge Poisoning","created":"2023-05-06","cited_by_count":10,"area":null},{"id":"2404.14389","title":"Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction","created":"2024-04-22","cited_by_count":10,"area":null},{"id":"2201.02775","title":"ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems","created":"2022-01-08","cited_by_count":9,"area":null},{"id":"2208.10993","title":"Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals","created":"2022-08-23","cited_by_count":9,"area":null},{"id":"2301.09732","title":"Backdoor Attacks in Peer-to-Peer Federated Learning","created":"2023-01-23","cited_by_count":9,"area":null},{"id":"2303.03908","title":"Client-specific Property Inference against Secure Aggregation in Federated Learning","created":"2023-03-07","cited_by_count":9,"area":null},{"id":"2311.16065","title":"A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective","created":"2023-11-27","cited_by_count":9,"area":null},{"id":"2311.18498","title":"Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach","created":"2023-11-30","cited_by_count":9,"area":null},{"id":"2401.16251","title":"Cross-silo Federated Learning with Record-level Personalized Differential Privacy","created":"2024-01-29","cited_by_count":9,"area":null},{"id":"2404.14016","title":"Ungeneralizable Examples","created":"2024-04-22","cited_by_count":9,"area":null},{"id":"2107.01477","title":"Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates","created":"2021-07-03","cited_by_count":8,"area":null},{"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":"2304.09515","title":"Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks","created":"2023-04-19","cited_by_count":8,"area":null},{"id":"2307.00543","title":"Defending Against Poisoning Attacks in Federated Learning with Blockchain","created":"2023-07-02","cited_by_count":8,"area":null},{"id":"2310.00542","title":"Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense","created":"2023-10-01","cited_by_count":8,"area":null},{"id":"2505.14585","title":"Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning","created":"2025-05-20","cited_by_count":8,"area":"lr"},{"id":"2506.15115","title":"Towards Reliable Forgetting: A Survey on Machine Unlearning Verification","created":"2025-06-18","cited_by_count":8,"area":"safety"},{"id":"2106.10734","title":"Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning","created":"2021-06-20","cited_by_count":7,"area":null},{"id":"2202.02817","title":"BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning","created":"2022-02-06","cited_by_count":7,"area":null},{"id":"2203.05192","title":"A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles","created":"2022-03-10","cited_by_count":7,"area":null},{"id":"2208.10273","title":"Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning","created":"2022-08-14","cited_by_count":7,"area":null},{"id":"2308.03573","title":"When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection","created":"2023-08-07","cited_by_count":7,"area":null},{"id":"2407.20830","title":"Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing","created":"2024-07-30","cited_by_count":7,"area":null},{"id":"2411.15673","title":"Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment","created":"2024-11-23","cited_by_count":7,"area":"mllm"},{"id":"2502.12430","title":"Position: Bridge the Gaps between Machine Unlearning and AI Regulation","created":"2025-02-18","cited_by_count":7,"area":"safety"},{"id":"2509.20383","title":"MARS: A Malignity-Aware Backdoor Defense in Federated Learning","created":"2025-09-21","cited_by_count":7,"area":null},{"id":"2104.10561","title":"Turning Federated Learning Systems Into Covert Channels","created":"2021-04-21","cited_by_count":6,"area":null},{"id":"2302.11571","title":"Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI","created":"2023-02-20","cited_by_count":6,"area":null},{"id":"2303.02725","title":"Local Environment Poisoning Attacks on Federated Reinforcement Learning","created":"2023-03-05","cited_by_count":6,"area":null},{"id":"2305.16272","title":"Incentivizing Honesty among Competitors in Collaborative Learning and Optimization","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2403.02983","title":"Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks","created":"2024-03-05","cited_by_count":6,"area":"safety"},{"id":"2410.15042","title":"Adversarial Training: A Survey","created":"2024-10-19","cited_by_count":6,"area":null},{"id":"2412.11476","title":"Vertical Federated Unlearning via Backdoor Certification","created":"2024-12-16","cited_by_count":6,"area":null},{"id":"2504.09839","title":"SafeSpeech: Robust and Universal Voice Protection Against Malicious Speech Synthesis","created":"2025-04-14","cited_by_count":6,"area":null},{"id":"2103.08820","title":"EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry","created":"2021-03-16","cited_by_count":5,"area":null},{"id":"2205.01184","title":"Performance Weighting for Robust Federated Learning Against Corrupted Sources","created":"2022-05-02","cited_by_count":5,"area":null},{"id":"2205.11736","title":"Towards a Defense Against Federated Backdoor Attacks Under Continuous Training","created":"2022-05-24","cited_by_count":5,"area":null},{"id":"2209.14093","title":"Securing Federated Learning against Overwhelming Collusive Attackers","created":"2022-09-28","cited_by_count":5,"area":null},{"id":"2210.16519","title":"Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance","created":"2022-10-29","cited_by_count":5,"area":null},{"id":"2405.16682","title":"A Systematic Review of Federated Generative Models","created":"2024-05-26","cited_by_count":5,"area":null},{"id":"2405.20975","title":"ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning","created":"2024-05-31","cited_by_count":5,"area":null}],"next_cursor":"100"}