{"total":21,"rows":[{"id":"2204.10607","title":"Federated Learning via Inexact ADMM","created":"2022-04-22","cited_by_count":44,"area":null},{"id":"2206.05091","title":"Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging","created":"2022-06-10","cited_by_count":27,"area":null},{"id":"2110.13986","title":"Fair Sequential Selection Using Supervised Learning Models","created":"2021-10-26","cited_by_count":16,"area":null},{"id":"2110.15318","title":"Communication-Efficient ADMM-based Federated Learning","created":"2021-10-28","cited_by_count":14,"area":null},{"id":"2205.01438","title":"FedGiA: An Efficient Hybrid Algorithm for Federated Learning","created":"2022-05-03","cited_by_count":8,"area":null},{"id":"2402.07471","title":"Differentially Private Decentralized Learning with Random Walks","created":"2024-02-12","cited_by_count":8,"area":null},{"id":"2402.10001","title":"Privacy Attacks in Decentralized Learning","created":"2024-02-15","cited_by_count":8,"area":null},{"id":"2105.14416","title":"Communication efficient privacy-preserving distributed optimization using adaptive differential quantization","created":"2021-05-30","cited_by_count":6,"area":null},{"id":"2204.12284","title":"Federated Stochastic Primal-dual Learning with Differential Privacy","created":"2022-04-26","cited_by_count":5,"area":null},{"id":"2302.12559","title":"From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning","created":"2023-02-24","cited_by_count":5,"area":null},{"id":"2408.08628","title":"A survey on secure decentralized optimization and learning","created":"2024-08-16","cited_by_count":4,"area":null},{"id":"2503.07505","title":"From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges","created":"2025-03-10","cited_by_count":4,"area":null},{"id":"2304.12534","title":"Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM","created":"2023-04-25","cited_by_count":3,"area":null},{"id":"2407.07421","title":"Federated PCA on Grassmann Manifold for IoT Anomaly Detection","created":"2024-07-10","cited_by_count":3,"area":null},{"id":"2106.01242","title":"A Privacy-Preserving and Trustable Multi-agent Learning Framework","created":"2021-06-02","cited_by_count":2,"area":null},{"id":"2403.00157","title":"Privacy-Preserving Distributed Optimization and Learning","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2208.11231","title":"Exact Penalty Method for Federated Learning","created":"2022-08-23","cited_by_count":1,"area":null},{"id":"2306.14094","title":"Locally Differentially Private Distributed Online Learning with Guaranteed Optimality","created":"2023-06-25","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":"2312.08685","title":"Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions","created":"2023-12-14","cited_by_count":1,"area":null},{"id":"2406.02599","title":"Privacy-Aware Randomized Quantization via Linear Programming","created":"2024-06-01","cited_by_count":1,"area":null}],"next_cursor":null}