{"total":19,"rows":[{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"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":"2111.09278","title":"Differentially Private Federated Learning on Heterogeneous Data","created":"2021-11-17","cited_by_count":74,"area":null},{"id":"2208.04591","title":"Stronger Privacy Amplification by Shuffling for R\\'enyi and Approximate Differential Privacy","created":"2022-08-09","cited_by_count":29,"area":null},{"id":"2106.02900","title":"Differentially Private Multi-Armed Bandits in the Shuffle Model","created":"2021-06-05","cited_by_count":24,"area":null},{"id":"2106.09805","title":"Shuffle Private Stochastic Convex Optimization","created":"2021-06-17","cited_by_count":17,"area":null},{"id":"2105.05180","title":"On the Renyi Differential Privacy of the Shuffle Model","created":"2021-05-11","cited_by_count":16,"area":null},{"id":"2107.08763","title":"Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning","created":"2021-07-19","cited_by_count":6,"area":null},{"id":"2411.01931","title":"Differentially private and decentralized randomized power method","created":"2024-11-04","cited_by_count":6,"area":null},{"id":"2109.00202","title":"Federated Learning: Issues in Medical Application","created":"2021-09-01","cited_by_count":4,"area":null},{"id":"2310.06237","title":"Differentially Private Multi-Site Treatment Effect Estimation","created":"2023-10-10","cited_by_count":4,"area":null},{"id":"2208.07922","title":"FedPerm: Private and Robust Federated Learning by Parameter Permutation","created":"2022-08-16","cited_by_count":3,"area":null},{"id":"2301.12535","title":"Concurrent Shuffle Differential Privacy Under Continual Observation","created":"2023-01-29","cited_by_count":2,"area":null},{"id":"2306.07959","title":"Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings","created":"2023-06-13","cited_by_count":2,"area":null},{"id":"2103.15690","title":"The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model","created":"2021-03-29","cited_by_count":1,"area":null},{"id":"2405.06636","title":"Federated Document Visual Question Answering: A Pilot Study","created":"2024-05-10","cited_by_count":1,"area":null},{"id":"2507.01487","title":"How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations","created":"2025-07-02","cited_by_count":1,"area":null},{"id":"2205.04855","title":"Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System","created":"2022-04-24","cited_by_count":0,"area":null},{"id":"2403.05344","title":"Federated Learning Method for Preserving Privacy in Face Recognition System","created":"2024-03-08","cited_by_count":0,"area":null}],"next_cursor":null}