{"total":13,"rows":[{"id":"2112.11256","title":"Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling","created":"2021-12-21","cited_by_count":66,"area":null},{"id":"2112.00193","title":"Public Data-Assisted Mirror Descent for Private Model Training","created":"2021-12-01","cited_by_count":49,"area":null},{"id":"2208.07984","title":"Private Estimation with Public Data","created":"2022-08-16","cited_by_count":32,"area":null},{"id":"2308.06239","title":"Private Distribution Learning with Public Data: The View from Sample Compression","created":"2023-08-11","cited_by_count":14,"area":null},{"id":"2404.13804","title":"Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks","created":"2024-04-22","cited_by_count":9,"area":null},{"id":"2305.09668","title":"Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free","created":"2023-04-27","cited_by_count":6,"area":null},{"id":"2310.13137","title":"Mean Estimation Under Heterogeneous Privacy Demands","created":"2023-10-19","cited_by_count":6,"area":null},{"id":"2110.15252","title":"Federated Learning with Heterogeneous Differential Privacy","created":"2021-10-28","cited_by_count":5,"area":null},{"id":"2201.12018","title":"Transfer Learning In Differential Privacy's Hybrid-Model","created":"2022-01-28","cited_by_count":3,"area":null},{"id":"2110.14426","title":"Locally Differentially Private Bayesian Inference","created":"2021-10-27","cited_by_count":1,"area":null},{"id":"2310.06341","title":"Federated Learning with Reduced Information Leakage and Computation","created":"2023-10-10","cited_by_count":1,"area":null},{"id":"2209.14086","title":"Momentum Gradient Descent Federated Learning with Local Differential Privacy","created":"2022-09-28","cited_by_count":0,"area":null},{"id":"2406.03405","title":"Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud","created":"2024-06-02","cited_by_count":0,"area":null}],"next_cursor":null}