{"total":20,"rows":[{"id":"2206.08307","title":"Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning","created":"2022-06-16","cited_by_count":79,"area":null},{"id":"2106.04159","title":"Fast Federated Learning in the Presence of Arbitrary Device Unavailability","created":"2021-06-08","cited_by_count":72,"area":null},{"id":"2205.13648","title":"A Unified Analysis of Federated Learning with Arbitrary Client Participation","created":"2022-05-26","cited_by_count":45,"area":null},{"id":"2108.09875","title":"Anarchic Federated Learning","created":"2021-08-23","cited_by_count":42,"area":null},{"id":"2310.20452","title":"AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms","created":"2023-10-31","cited_by_count":16,"area":null},{"id":"2312.12670","title":"On the Role of Server Momentum in Federated Learning","created":"2023-12-19","cited_by_count":16,"area":null},{"id":"2206.05891","title":"Anchor Sampling for Federated Learning with Partial Client Participation","created":"2022-06-13","cited_by_count":14,"area":null},{"id":"2302.03662","title":"Federated Learning with Regularized Client Participation","created":"2023-02-07","cited_by_count":14,"area":null},{"id":"2112.11989","title":"FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation","created":"2021-12-22","cited_by_count":10,"area":null},{"id":"2211.13975","title":"FedGS: Federated Graph-based Sampling with Arbitrary Client Availability","created":"2022-11-25","cited_by_count":10,"area":null},{"id":"2306.12212","title":"MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates","created":"2023-06-21","cited_by_count":10,"area":null},{"id":"2409.17446","title":"Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability","created":"2024-09-26","cited_by_count":9,"area":null},{"id":"2410.23131","title":"Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis","created":"2024-10-30","cited_by_count":8,"area":null},{"id":"2305.19971","title":"Federated Learning in the Presence of Adversarial Client Unavailability","created":"2023-05-31","cited_by_count":5,"area":null},{"id":"2205.13222","title":"Combating Client Dropout in Federated Learning via Friend Model Substitution","created":"2022-05-26","cited_by_count":3,"area":null},{"id":"2212.13679","title":"CC-FedAvg: Computationally Customized Federated Averaging","created":"2022-12-28","cited_by_count":2,"area":null},{"id":"2309.07197","title":"Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments","created":"2023-09-13","cited_by_count":2,"area":null},{"id":"2406.03766","title":"Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks","created":"2024-06-06","cited_by_count":2,"area":null},{"id":"2306.03401","title":"A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging","created":"2023-06-06","cited_by_count":1,"area":null},{"id":"2407.16728","title":"Distributed Difference of Convex Optimization","created":"2024-07-23","cited_by_count":0,"area":null}],"next_cursor":null}