{"total":23,"rows":[{"id":"2102.06387","title":"The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation","created":"2021-02-12","cited_by_count":128,"area":null},{"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":"2109.14236","title":"LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning","created":"2021-09-29","cited_by_count":64,"area":null},{"id":"2202.07178","title":"Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy","created":"2022-02-15","cited_by_count":33,"area":null},{"id":"2111.01108","title":"Resource-Efficient Federated Learning","created":"2021-11-01","cited_by_count":26,"area":null},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26,"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":"2101.07750","title":"Information Theoretic Secure Aggregation with User Dropouts","created":"2021-01-19","cited_by_count":19,"area":null},{"id":"2204.06760","title":"HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks","created":"2022-04-14","cited_by_count":12,"area":null},{"id":"2210.10514","title":"The Future of Consumer Edge-AI Computing","created":"2022-10-19","cited_by_count":12,"area":null},{"id":"2108.12978","title":"Private Multi-Task Learning: Formulation and Applications to Federated Learning","created":"2021-08-30","cited_by_count":11,"area":null},{"id":"2110.02177","title":"Secure Aggregation for Buffered Asynchronous Federated Learning","created":"2021-10-05","cited_by_count":10,"area":null},{"id":"2102.11274","title":"Sustainable Federated Learning","created":"2021-02-22","cited_by_count":5,"area":null},{"id":"2502.03340","title":"Interaction-Aware Gaussian Weighting for Clustered Federated Learning","created":"2025-02-05","cited_by_count":5,"area":null},{"id":"2109.14046","title":"Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources","created":"2021-09-28","cited_by_count":4,"area":null},{"id":"2207.12779","title":"Reconciling Security and Communication Efficiency in Federated Learning","created":"2022-07-26","cited_by_count":4,"area":null},{"id":"2306.03191","title":"Personalized Federated Domain Adaptation for Item-to-Item Recommendation","created":"2023-06-05","cited_by_count":4,"area":null},{"id":"2509.21389","title":"Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2505.13655","title":"Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2107.05457","title":"Improving the Algorithm of Deep Learning with Differential Privacy","created":"2021-07-12","cited_by_count":0,"area":null},{"id":"2204.02632","title":"DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg","created":"2022-04-06","cited_by_count":0,"area":null},{"id":"2307.06283","title":"Tackling Computational Heterogeneity in FL: A Few Theoretical Insights","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2606.07277","title":"The Capacity of Information-Theoretic Secure Aggregation in Federated Learning","created":"2026-06-05","cited_by_count":0,"area":null}],"next_cursor":null}