{"total":21,"rows":[{"id":"2110.14216","title":"What Do We Mean by Generalization in Federated Learning?","created":"2021-10-27","cited_by_count":62,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2112.09327","title":"Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards","created":"2021-12-17","cited_by_count":20,"area":null},{"id":"2210.02680","title":"DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing","created":"2022-10-06","cited_by_count":17,"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":"2105.02295","title":"Byzantine-Robust and Privacy-Preserving Framework for FedML","created":"2021-05-05","cited_by_count":14,"area":null},{"id":"2105.03875","title":"Bounding Information Leakage in Machine Learning","created":"2021-05-09","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":"2109.09868","title":"ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems","created":"2021-09-20","cited_by_count":5,"area":null},{"id":"2406.00300","title":"Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework","created":"2024-06-01","cited_by_count":5,"area":null},{"id":"2101.11653","title":"List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier","created":"2021-01-27","cited_by_count":4,"area":null},{"id":"2107.12958","title":"Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning","created":"2021-07-27","cited_by_count":4,"area":null},{"id":"2502.08058","title":"General Coded Computing: Adversarial Settings","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2208.10731","title":"FedMCSA: Personalized Federated Learning via Model Components Self-Attention","created":"2022-08-23","cited_by_count":3,"area":null},{"id":"2204.02497","title":"Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption","created":"2022-04-05","cited_by_count":1,"area":null},{"id":"2107.12806","title":"Towards Industrial Private AI: A two-tier framework for data and model security","created":"2021-07-27","cited_by_count":0,"area":null},{"id":"2204.11168","title":"Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation","created":"2022-04-24","cited_by_count":0,"area":null},{"id":"2402.04377","title":"NeRCC: Nested-Regression Coded Computing for Resilient Distributed Prediction Serving Systems","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2506.15181","title":"ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2608.28910","title":"Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.00552","title":"Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing","created":"2026-09-01","cited_by_count":0,"area":null}],"next_cursor":null}