{"total":46,"rows":[{"id":"2106.05203","title":"EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback","created":"2021-06-09","cited_by_count":133,"area":null},{"id":"2210.04620","title":"FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings","created":"2022-10-10","cited_by_count":125,"area":null},{"id":"2106.04159","title":"Fast Federated Learning in the Presence of Arbitrary Device Unavailability","created":"2021-06-08","cited_by_count":72,"area":null},{"id":"2106.02969","title":"FedNL: Making Newton-Type Methods Applicable to Federated Learning","created":"2021-06-05","cited_by_count":52,"area":null},{"id":"2206.03611","title":"FedPop: A Bayesian Approach for Personalised Federated Learning","created":"2022-06-07","cited_by_count":35,"area":null},{"id":"2206.03665","title":"Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression","created":"2022-06-08","cited_by_count":33,"area":null},{"id":"2207.04338","title":"Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning","created":"2022-07-09","cited_by_count":33,"area":null},{"id":"2305.15155","title":"Momentum Provably Improves Error Feedback!","created":"2023-05-24","cited_by_count":31,"area":null},{"id":"2302.00543","title":"DoCoFL: Downlink Compression for Cross-Device Federated Learning","created":"2023-02-01","cited_by_count":28,"area":null},{"id":"2110.05323","title":"ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training","created":"2021-10-11","cited_by_count":27,"area":null},{"id":"2106.00797","title":"QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning","created":"2021-06-01","cited_by_count":26,"area":null},{"id":"2103.08870","title":"Learned Gradient Compression for Distributed Deep Learning","created":"2021-03-16","cited_by_count":20,"area":null},{"id":"2207.03957","title":"Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox","created":"2022-07-08","cited_by_count":19,"area":null},{"id":"2205.04180","title":"EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization","created":"2022-05-09","cited_by_count":17,"area":null},{"id":"2211.00188","title":"Adaptive Compression for Communication-Efficient Distributed Training","created":"2022-10-31","cited_by_count":16,"area":null},{"id":"2409.09727","title":"From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare","created":"2024-09-15","cited_by_count":16,"area":null},{"id":"2110.03313","title":"Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees","created":"2021-10-07","cited_by_count":15,"area":null},{"id":"2111.01847","title":"Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning","created":"2021-11-02","cited_by_count":15,"area":null},{"id":"2302.08545","title":"THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression","created":"2023-02-16","cited_by_count":15,"area":null},{"id":"2106.03056","title":"MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization","created":"2021-06-06","cited_by_count":14,"area":null},{"id":"2206.05891","title":"Anchor Sampling for Federated Learning with Partial Client Participation","created":"2022-06-13","cited_by_count":14,"area":null},{"id":"2305.16297","title":"Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?","created":"2023-05-25","cited_by_count":14,"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":"2305.07612","title":"Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression","created":"2023-05-12","cited_by_count":11,"area":null},{"id":"2206.10032","title":"Communication-Efficient Federated Learning With Data and Client Heterogeneity","created":"2022-06-20","cited_by_count":9,"area":null},{"id":"2110.03294","title":"EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback","created":"2021-10-07","cited_by_count":8,"area":null},{"id":"2206.09446","title":"Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities","created":"2022-06-19","cited_by_count":8,"area":null},{"id":"2211.03942","title":"Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design","created":"2022-11-08","cited_by_count":6,"area":null},{"id":"2306.00280","title":"Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications","created":"2023-06-01","cited_by_count":6,"area":null},{"id":"2111.02083","title":"Federated Expectation Maximization with heterogeneity mitigation and variance reduction","created":"2021-11-03","cited_by_count":5,"area":null},{"id":"2305.19971","title":"Federated Learning in the Presence of Adversarial Client Unavailability","created":"2023-05-31","cited_by_count":5,"area":null},{"id":"2210.00665","title":"Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms","created":"2022-10-03","cited_by_count":4,"area":null},{"id":"2302.09832","title":"TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation","created":"2023-02-20","cited_by_count":4,"area":null},{"id":"2303.04622","title":"ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression","created":"2023-03-08","cited_by_count":4,"area":null},{"id":"2404.10091","title":"Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics","created":"2024-04-15","cited_by_count":3,"area":null},{"id":"2405.16339","title":"BOLD: Boolean Logic Deep Learning","created":"2024-05-25","cited_by_count":3,"area":null},{"id":"2109.08346","title":"Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching","created":"2021-09-17","cited_by_count":2,"area":null},{"id":"2110.07649","title":"Federated learning and next generation wireless communications: A survey on bidirectional relationship","created":"2021-10-14","cited_by_count":2,"area":null},{"id":"2302.09634","title":"Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity","created":"2023-02-19","cited_by_count":2,"area":null},{"id":"2304.03638","title":"Compressed Regression over Adaptive Networks","created":"2023-04-07","cited_by_count":2,"area":null},{"id":"2402.11857","title":"Communication-Efficient Distributed Learning with Local Immediate Error Compensation","created":"2024-02-19","cited_by_count":2,"area":null},{"id":"2506.05271","title":"Tight analyses of first-order methods with error feedback","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2601.12400","title":"BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training","created":"2026-01-18","cited_by_count":1,"area":null},{"id":"2209.15203","title":"Downlink Compression Improves TopK Sparsification","created":"2022-09-30","cited_by_count":0,"area":null},{"id":"2605.31594","title":"A Tight Theory of Error Feedback Algorithms in Distributed Optimization","created":"2026-05-29","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}