{"total":20,"rows":[{"id":"2109.05700","title":"Exploiting Heterogeneity in Robust Federated Best-Arm Identification","created":"2021-09-13","cited_by_count":20,"area":null},{"id":"2105.07320","title":"LocalNewton: Reducing Communication Bottleneck for Distributed Learning","created":"2021-05-16","cited_by_count":15,"area":null},{"id":"2310.09804","title":"Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates","created":"2023-10-15","cited_by_count":12,"area":null},{"id":"2203.15488","title":"Over-the-Air Federated Learning via Second-Order Optimization","created":"2022-03-29","cited_by_count":11,"area":null},{"id":"2206.03588","title":"Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation","created":"2022-06-07","cited_by_count":11,"area":null},{"id":"2302.11466","title":"Advancements in Federated Learning: Models, Methods, and Privacy","created":"2023-02-22","cited_by_count":7,"area":null},{"id":"2206.02834","title":"Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds","created":"2022-06-06","cited_by_count":5,"area":null},{"id":"2501.10877","title":"Distributed Quasi-Newton Method for Fair and Fast Federated Learning","created":"2025-01-18","cited_by_count":5,"area":null},{"id":"2303.05206","title":"FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning","created":"2023-03-09","cited_by_count":4,"area":null},{"id":"2103.09424","title":"Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience","created":"2021-03-17","cited_by_count":3,"area":null},{"id":"2109.05222","title":"Fundamental limits of over-the-air optimization: Are analog schemes optimal?","created":"2021-09-11","cited_by_count":3,"area":null},{"id":"2401.02734","title":"FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning","created":"2024-01-05","cited_by_count":3,"area":null},{"id":"2206.00529","title":"Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top","created":"2022-06-01","cited_by_count":2,"area":null},{"id":"2106.06891","title":"Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning","created":"2021-06-13","cited_by_count":1,"area":null},{"id":"2307.07941","title":"Optimal Compression of Unit Norm Vectors in the High Distortion Regime","created":"2023-07-16","cited_by_count":1,"area":null},{"id":"2412.03867","title":"GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning","created":"2024-12-05","cited_by_count":1,"area":null},{"id":"2210.16682","title":"Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks","created":"2022-10-29","cited_by_count":0,"area":null},{"id":"2405.06569","title":"Efficient Federated Low Rank Matrix Completion","created":"2024-05-10","cited_by_count":0,"area":null},{"id":"2409.15216","title":"FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch","created":"2024-09-23","cited_by_count":0,"area":null},{"id":"2602.03329","title":"From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity","created":"2026-02-03","cited_by_count":0,"area":null}],"next_cursor":null}