{"total":18,"rows":[{"id":"2302.03109","title":"On the Convergence of Federated Averaging with Cyclic Client Participation","created":"2023-02-06","cited_by_count":31,"area":null},{"id":"2206.07021","title":"Federated Optimization Algorithms with Random Reshuffling and Gradient Compression","created":"2022-06-14","cited_by_count":18,"area":null},{"id":"2302.03662","title":"Federated Learning with Regularized Client Participation","created":"2023-02-07","cited_by_count":14,"area":null},{"id":"2104.12112","title":"Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders","created":"2021-04-25","cited_by_count":13,"area":null},{"id":"2410.08305","title":"Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation","created":"2024-10-10","cited_by_count":13,"area":null},{"id":"2205.03914","title":"Federated Random Reshuffling with Compression and Variance Reduction","created":"2022-05-08","cited_by_count":12,"area":null},{"id":"2306.03240","title":"Improving Accelerated Federated Learning with Compression and Importance Sampling","created":"2023-06-05","cited_by_count":8,"area":null},{"id":"2111.13322","title":"Random-reshuffled SARAH does not need a full gradient computations","created":"2021-11-26","cited_by_count":7,"area":null},{"id":"2306.16484","title":"Towards a Better Theoretical Understanding of Independent Subnetwork Training","created":"2023-06-28","cited_by_count":6,"area":null},{"id":"2311.11841","title":"High Probability Guarantees for Random Reshuffling","created":"2023-11-20","cited_by_count":5,"area":null},{"id":"2310.15976","title":"Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization","created":"2023-10-24","cited_by_count":2,"area":null},{"id":"2403.06677","title":"Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction","created":"2024-03-11","cited_by_count":2,"area":null},{"id":"2502.14648","title":"Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2212.01848","title":"Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness","created":"2022-12-04","cited_by_count":0,"area":null},{"id":"2401.07694","title":"Stochastic optimization with arbitrary recurrent data sampling","created":"2024-01-15","cited_by_count":0,"area":null},{"id":"2506.12444","title":"Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting","created":"2025-06-14","cited_by_count":0,"area":null},{"id":"2507.08913","title":"Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness","created":"2025-07-11","cited_by_count":0,"area":null},{"id":"2509.04133","title":"Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees","created":"2025-09-04","cited_by_count":0,"area":null}],"next_cursor":null}