{"total":21,"rows":[{"id":"2309.13591","title":"Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity","created":"2023-09-24","cited_by_count":26,"area":null},{"id":"2405.01031","title":"The Privacy Power of Correlated Noise in Decentralized Learning","created":"2024-05-02","cited_by_count":18,"area":null},{"id":"2405.14432","title":"Adaptive Gradient Clipping for Robust Federated Learning","created":"2024-05-23","cited_by_count":11,"area":null},{"id":"2308.14606","title":"On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning","created":"2023-08-28","cited_by_count":8,"area":null},{"id":"2505.01874","title":"Towards Trustworthy Federated Learning with Untrusted Participants","created":"2025-05-03","cited_by_count":8,"area":null},{"id":"2410.22699","title":"Exactly Minimax-Optimal Locally Differentially Private Sampling","created":"2024-10-30","cited_by_count":5,"area":null},{"id":"2312.12835","title":"Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers","created":"2023-12-20","cited_by_count":4,"area":null},{"id":"2408.08628","title":"A survey on secure decentralized optimization and learning","created":"2024-08-16","cited_by_count":4,"area":null},{"id":"2505.24802","title":"ByzFL: Research Framework for Robust Federated Learning","created":"2025-05-30","cited_by_count":4,"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":"2312.14712","title":"Balancing Privacy, Robustness, and Efficiency in Machine Learning","created":"2023-12-22","cited_by_count":1,"area":null},{"id":"2402.13700","title":"On the Conflict of Robustness and Learning in Collaborative Machine Learning","created":"2024-02-21","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":"2309.05395","title":"SABLE: Secure And Byzantine robust LEarning","created":"2023-09-11","cited_by_count":0,"area":null},{"id":"2408.04963","title":"LiD-FL: Towards List-Decodable Federated Learning","created":"2024-08-09","cited_by_count":0,"area":null},{"id":"2506.11413","title":"Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2508.12978","title":"Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning","created":"2025-08-18","cited_by_count":0,"area":null},{"id":"2511.21799","title":"The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2605.19813","title":"A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2606.28123","title":"Dangerous Liaisons of Convex Learning and Non-Affine Aggregation","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2607.01492","title":"Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness","created":"2026-07-01","cited_by_count":0,"area":null}],"next_cursor":null}