{"total":64,"rows":[{"id":"2201.08135","title":"Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges","created":"2022-01-20","cited_by_count":84,"area":null},{"id":"2111.07380","title":"Eluding Secure Aggregation in Federated Learning via Model Inconsistency","created":"2021-11-14","cited_by_count":67,"area":null},{"id":"2202.06924","title":"Do Gradient Inversion Attacks Make Federated Learning Unsafe?","created":"2022-02-14","cited_by_count":44,"area":null},{"id":"2208.10888","title":"Joint Privacy Enhancement and Quantization in Federated Learning","created":"2022-08-23","cited_by_count":35,"area":null},{"id":"2206.04621","title":"A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning","created":"2022-06-09","cited_by_count":27,"area":null},{"id":"2210.07714","title":"CrowdGuard: Federated Backdoor Detection in Federated Learning","created":"2022-10-14","cited_by_count":27,"area":null},{"id":"2312.04432","title":"FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning","created":"2023-12-07","cited_by_count":27,"area":null},{"id":"2107.03311","title":"RoFL: Robustness of Secure Federated Learning","created":"2021-07-07","cited_by_count":25,"area":null},{"id":"2205.08443","title":"On the (In)security of Peer-to-Peer Decentralized Machine Learning","created":"2022-05-17","cited_by_count":23,"area":null},{"id":"2301.09508","title":"BayBFed: Bayesian Backdoor Defense for Federated Learning","created":"2023-01-23","cited_by_count":23,"area":null},{"id":"2304.12961","title":"Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning","created":"2023-04-25","cited_by_count":21,"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":"2405.03636","title":"The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape","created":"2024-05-06","cited_by_count":13,"area":null},{"id":"2209.04030","title":"Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks","created":"2022-09-08","cited_by_count":12,"area":null},{"id":"2304.08847","title":"BadVFL: Backdoor Attacks in Vertical Federated Learning","created":"2023-04-18","cited_by_count":10,"area":null},{"id":"2405.15182","title":"RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation","created":"2024-05-24","cited_by_count":10,"area":null},{"id":"2310.13424","title":"FLTracer: Accurate Poisoning Attack Provenance in Federated Learning","created":"2023-10-20","cited_by_count":8,"area":null},{"id":"2404.06430","title":"pfl-research: simulation framework for accelerating research in Private Federated Learning","created":"2024-04-09","cited_by_count":8,"area":null},{"id":"2402.07002","title":"Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off","created":"2024-02-10","cited_by_count":7,"area":null},{"id":"2507.22908","title":"A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection","created":"2025-07-15","cited_by_count":7,"area":null},{"id":"2302.09844","title":"FederatedTrust: A Solution for Trustworthy Federated Learning","created":"2023-02-20","cited_by_count":6,"area":null},{"id":"2305.00873","title":"Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy","created":"2023-05-01","cited_by_count":5,"area":null},{"id":"2404.16847","title":"State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey","created":"2024-02-25","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":"2207.05978","title":"Enhanced Security and Privacy via Fragmented Federated Learning","created":"2022-07-13","cited_by_count":4,"area":null},{"id":"2208.05320","title":"PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning","created":"2022-08-09","cited_by_count":4,"area":null},{"id":"2209.03050","title":"Cerberus: Exploring Federated Prediction of Security Events","created":"2022-09-07","cited_by_count":4,"area":null},{"id":"2310.02113","title":"FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks","created":"2023-10-03","cited_by_count":4,"area":null},{"id":"2503.07505","title":"From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges","created":"2025-03-10","cited_by_count":4,"area":null},{"id":"2207.12327","title":"Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment","created":"2022-07-25","cited_by_count":3,"area":null},{"id":"2208.07922","title":"FedPerm: Private and Robust Federated Learning by Parameter Permutation","created":"2022-08-16","cited_by_count":3,"area":null},{"id":"2411.14516","title":"Memory Backdoor Attacks on Neural Networks","created":"2024-11-21","cited_by_count":3,"area":null},{"id":"2112.07718","title":"Scatterbrained: A flexible and expandable pattern for decentralized machine learning","created":"2021-12-14","cited_by_count":2,"area":null},{"id":"2308.16671","title":"Sparse Decentralized Federated Learning","created":"2023-08-31","cited_by_count":2,"area":null},{"id":"2402.10816","title":"TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data","created":"2024-02-16","cited_by_count":2,"area":null},{"id":"2407.09958","title":"Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning","created":"2024-07-13","cited_by_count":2,"area":null},{"id":"2411.05260","title":"QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning","created":"2024-11-08","cited_by_count":2,"area":null},{"id":"2512.01025","title":"Operator-Theoretic Framework for Gradient-Free Federated Learning","created":"2025-11-30","cited_by_count":2,"area":null},{"id":"2208.11231","title":"Exact Penalty Method for Federated Learning","created":"2022-08-23","cited_by_count":1,"area":null},{"id":"2210.13457","title":"Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning","created":"2022-10-22","cited_by_count":1,"area":null},{"id":"2312.14712","title":"Balancing Privacy, Robustness, and Efficiency in Machine Learning","created":"2023-12-22","cited_by_count":1,"area":null},{"id":"2407.14710","title":"Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence","created":"2024-07-20","cited_by_count":1,"area":null},{"id":"2408.14240","title":"Celtibero: Robust Layered Aggregation for Federated Learning","created":"2024-08-26","cited_by_count":1,"area":null},{"id":"2506.13561","title":"Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning","created":"2025-06-16","cited_by_count":1,"area":null},{"id":"2507.04903","title":"BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2509.02411","title":"A Survey: Towards Privacy and Security in Mobile Large Language Models","created":"2025-09-02","cited_by_count":1,"area":null},{"id":"2605.05769","title":"Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2605.07674","title":"Differentially Private Auditing Under Strategic Response","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2302.14514","title":"Differentially Private Distributed Convex Optimization","created":"2023-02-28","cited_by_count":0,"area":null},{"id":"2401.11592","title":"Differentially-Private Multi-Tier Federated Learning","created":"2024-01-21","cited_by_count":0,"area":null},{"id":"2502.05211","title":"Decoding FL Defenses: Systemization, Pitfalls, and Remedies","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2504.15674","title":"TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data","created":"2025-04-22","cited_by_count":0,"area":null},{"id":"2505.14024","title":"FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2509.05265","title":"On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy","created":"2025-09-05","cited_by_count":0,"area":null},{"id":"2509.09787","title":"ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)","created":"2025-09-11","cited_by_count":0,"area":null},{"id":"2510.09691","title":"Evaluation of Differential Privacy Mechanisms on Federated Learning","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2512.13734","title":"Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation","created":"2025-12-14","cited_by_count":0,"area":null},{"id":"2601.09166","title":"DP-FedSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix","created":"2026-01-14","cited_by_count":0,"area":null},{"id":"2602.05410","title":"Robust Federated Learning via Byzantine Filtering over Encrypted Updates","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2603.13570","title":"Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap","created":"2026-03-13","cited_by_count":0,"area":null},{"id":"2603.19949","title":"TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2604.07125","title":"Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2605.21780","title":"Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2606.17035","title":"Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning","created":"2026-06-15","cited_by_count":0,"area":null}],"next_cursor":null}