{"total":29,"rows":[{"id":"2202.09357","title":"ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!","created":"2022-02-18","cited_by_count":134,"area":null},{"id":"2111.03741","title":"Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective","created":"2021-11-05","cited_by_count":38,"area":null},{"id":"2110.10342","title":"Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond","created":"2021-10-20","cited_by_count":36,"area":null},{"id":"2205.05040","title":"A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks","created":"2022-05-10","cited_by_count":20,"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":"2209.02257","title":"Faster federated optimization under second-order similarity","created":"2022-09-06","cited_by_count":18,"area":null},{"id":"2301.00457","title":"ReSQueing Parallel and Private Stochastic Convex Optimization","created":"2023-01-01","cited_by_count":17,"area":null},{"id":"2203.15260","title":"Efficient Convex Optimization Requires Superlinear Memory","created":"2022-03-29","cited_by_count":14,"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":"2411.18607","title":"Task Arithmetic Through The Lens Of One-Shot Federated Learning","created":"2024-11-27","cited_by_count":12,"area":null},{"id":"2108.06869","title":"FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning","created":"2021-08-16","cited_by_count":11,"area":null},{"id":"2109.02388","title":"On Second-order Optimization Methods for Federated Learning","created":"2021-09-06","cited_by_count":9,"area":null},{"id":"2302.07155","title":"EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data","created":"2023-02-14","cited_by_count":7,"area":null},{"id":"2302.07615","title":"Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities","created":"2023-02-15","cited_by_count":7,"area":null},{"id":"2311.17586","title":"Federated Online and Bandit Convex Optimization","created":"2023-11-29","cited_by_count":7,"area":null},{"id":"2110.02954","title":"A Stochastic Newton Algorithm for Distributed Convex Optimization","created":"2021-10-07","cited_by_count":6,"area":null},{"id":"2404.09494","title":"On the Necessity of Collaboration for Online Model Selection with Decentralized Data","created":"2024-04-15","cited_by_count":6,"area":null},{"id":"2302.04963","title":"Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal","created":"2023-02-09","cited_by_count":5,"area":null},{"id":"2109.01326","title":"Statistical Estimation and Inference via Local SGD in Federated Learning","created":"2021-09-03","cited_by_count":2,"area":null},{"id":"2305.10294","title":"DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime","created":"2023-05-17","cited_by_count":2,"area":null},{"id":"2308.09187","title":"Distributed Extra-gradient with Optimal Complexity and Communication Guarantees","created":"2023-08-17","cited_by_count":2,"area":null},{"id":"2511.13999","title":"On the Gradient Complexity of Private Optimization with Private Oracles","created":"2025-11-17","cited_by_count":2,"area":null},{"id":"2103.05138","title":"On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization","created":"2021-03-08","cited_by_count":1,"area":null},{"id":"2501.03222","title":"Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization","created":"2025-01-06","cited_by_count":1,"area":null},{"id":"2505.14371","title":"Layer-wise Quantization for Quantized Optimistic Dual Averaging","created":"2025-05-20","cited_by_count":1,"area":null},{"id":"2407.02689","title":"Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps","created":"2024-07-02","cited_by_count":0,"area":null},{"id":"2606.01128","title":"Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2607.24827","title":"Stronger Memory-Query Tradeoffs for Convex Optimization: The Limitations of Subquadratic Memory","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2608.06563","title":"Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization","created":"2026-08-06","cited_by_count":0,"area":null}],"next_cursor":null}