{"total":42,"rows":[{"id":"2206.02618","title":"Generalized Federated Learning via Sharpness Aware Minimization","created":"2022-06-06","cited_by_count":144,"area":null},{"id":"2308.08165","title":"Stochastic Controlled Averaging for Federated Learning with Communication Compression","created":"2023-08-16","cited_by_count":101,"area":null},{"id":"2207.14130","title":"FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning","created":"2022-07-28","cited_by_count":64,"area":null},{"id":"2206.05187","title":"On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond","created":"2022-06-10","cited_by_count":38,"area":null},{"id":"2306.16504","title":"Momentum Benefits Non-IID Federated Learning Simply and Provably","created":"2023-06-28","cited_by_count":38,"area":null},{"id":"2212.00974","title":"Faster Adaptive Federated Learning","created":"2022-12-02","cited_by_count":35,"area":null},{"id":"2201.03172","title":"Communication-Efficient Federated Learning with Accelerated Client Gradient","created":"2022-01-10","cited_by_count":24,"area":null},{"id":"2207.07223","title":"Accelerated Federated Learning with Decoupled Adaptive Optimization","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2202.01268","title":"DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization","created":"2022-02-02","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":"2405.11667","title":"The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication","created":"2024-05-19","cited_by_count":12,"area":null},{"id":"2210.00690","title":"Taming Fat-Tailed (\"Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning","created":"2022-10-03","cited_by_count":10,"area":null},{"id":"2310.03613","title":"Solving a Class of Non-Convex Minimax Optimization in Federated Learning","created":"2023-10-05","cited_by_count":10,"area":null},{"id":"2302.06701","title":"Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems","created":"2023-02-13","cited_by_count":8,"area":null},{"id":"2311.05924","title":"Federated Learning with Manifold Regularization and Normalized Update Reaggregation","created":"2023-11-10","cited_by_count":8,"area":null},{"id":"2211.01122","title":"Fast Adaptive Federated Bilevel Optimization","created":"2022-11-02","cited_by_count":7,"area":null},{"id":"2212.02376","title":"DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization","created":"2022-12-05","cited_by_count":7,"area":null},{"id":"2306.02570","title":"When Decentralized Optimization Meets Federated Learning","created":"2023-06-05","cited_by_count":7,"area":null},{"id":"2510.03866","title":"On Provable Benefits of Muon in Federated Learning","created":"2025-10-04","cited_by_count":7,"area":null},{"id":"2603.05116","title":"FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning","created":"2026-03-05","cited_by_count":7,"area":null},{"id":"2402.09941","title":"FedLion: Faster Adaptive Federated Optimization with Fewer Communication","created":"2024-02-15","cited_by_count":6,"area":null},{"id":"2211.07303","title":"Adaptive Federated Minimax Optimization with Lower Complexities","created":"2022-11-14","cited_by_count":5,"area":null},{"id":"2310.02524","title":"Federated Conditional Stochastic Optimization","created":"2023-10-04","cited_by_count":4,"area":null},{"id":"2206.09576","title":"FedSSO: A Federated Server-Side Second-Order Optimization Algorithm","created":"2022-06-20","cited_by_count":3,"area":null},{"id":"2402.09247","title":"Momentum Approximation in Asynchronous Private Federated Learning","created":"2024-02-14","cited_by_count":3,"area":null},{"id":"2405.02745","title":"Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation","created":"2024-05-04","cited_by_count":3,"area":null},{"id":"2511.22616","title":"Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers","created":"2025-11-27","cited_by_count":3,"area":null},{"id":"2211.01883","title":"Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2302.06103","title":"FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging","created":"2023-02-13","cited_by_count":2,"area":null},{"id":"2309.10679","title":"Oracle Complexity Reduction for Model-free LQR: A Stochastic Variance-Reduced Policy Gradient Approach","created":"2023-09-19","cited_by_count":2,"area":null},{"id":"2412.11660","title":"Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning","created":"2024-12-16","cited_by_count":2,"area":null},{"id":"2204.13299","title":"On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems","created":"2022-04-28","cited_by_count":1,"area":null},{"id":"2311.12652","title":"FedDRO: Federated Compositional Optimization for Distributionally Robust Learning","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2507.22855","title":"Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach","created":"2025-07-30","cited_by_count":1,"area":null},{"id":"2210.00611","title":"SAGDA: Achieving $\\mathcal{O}(\\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning","created":"2022-10-02","cited_by_count":0,"area":null},{"id":"2404.11888","title":"FedEGG: Federated Learning with Explicit Global Guidance","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2408.15621","title":"Convergent Differential Privacy Analysis for General Federated Learning","created":"2024-08-28","cited_by_count":0,"area":null},{"id":"2504.17528","title":"TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction","created":"2025-04-24","cited_by_count":0,"area":null},{"id":"2508.07505","title":"Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach","created":"2025-08-10","cited_by_count":0,"area":null},{"id":"2510.04203","title":"Adaptive Federated Learning via Dynamical System Model","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2608.12710","title":"Federated Compositional Muon Optimizer for Matrix-Wise Models","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.22945","title":"A Momentum-Based Variance-Reduced Algorithm for Federated Multiobjective Optimization","created":"2026-08-24","cited_by_count":0,"area":null}],"next_cursor":null}