{"total":45,"rows":[{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"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":"2102.07845","title":"MARINA: Faster Non-Convex Distributed Learning with Compression","created":"2021-02-15","cited_by_count":78,"area":null},{"id":"2108.00951","title":"Rethinking gradient sparsification as total error minimization","created":"2021-08-02","cited_by_count":41,"area":null},{"id":"2202.07262","title":"Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods","created":"2022-02-15","cited_by_count":37,"area":null},{"id":"2103.03239","title":"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices","created":"2021-03-04","cited_by_count":36,"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":"2305.15155","title":"Momentum Provably Improves Error Feedback!","created":"2023-05-24","cited_by_count":31,"area":null},{"id":"2206.01299","title":"Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees","created":"2022-06-02","cited_by_count":30,"area":null},{"id":"2107.09461","title":"CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression","created":"2021-07-20","cited_by_count":23,"area":null},{"id":"2102.07245","title":"Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization","created":"2021-02-14","cited_by_count":21,"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":"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":"2301.09830","title":"Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression","created":"2023-01-24","cited_by_count":15,"area":null},{"id":"2311.05645","title":"EControl: Fast Distributed Optimization with Compression and Error Control","created":"2023-11-06","cited_by_count":15,"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":"2102.09234","title":"ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks","created":"2021-02-18","cited_by_count":13,"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":"2206.03588","title":"Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation","created":"2022-06-07","cited_by_count":11,"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":"2305.18929","title":"Clip21: Error Feedback for Gradient Clipping","created":"2023-05-30","cited_by_count":11,"area":null},{"id":"2312.14567","title":"Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise","created":"2023-12-22","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":"2212.01523","title":"GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning","created":"2022-12-03","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":"2306.16484","title":"Towards a Better Theoretical Understanding of Independent Subnetwork Training","created":"2023-06-28","cited_by_count":6,"area":null},{"id":"2203.02383","title":"Distributed Methods with Absolute Compression and Error Compensation","created":"2022-03-04","cited_by_count":4,"area":null},{"id":"2410.13743","title":"Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications","created":"2024-10-17","cited_by_count":4,"area":null},{"id":"2410.16871","title":"Error Feedback under $(L_0,L_1)$-Smoothness: Normalization and Momentum","created":"2024-10-22","cited_by_count":4,"area":null},{"id":"2503.08427","title":"Accelerated Distributed Optimization with Compression and Error Feedback","created":"2025-03-11","cited_by_count":4,"area":null},{"id":"2502.13482","title":"Smoothed Normalization for Efficient Distributed Private Optimization","created":"2025-02-19","cited_by_count":3,"area":null},{"id":"2505.21684","title":"Incentivizing Permissionless Distributed Learning of LLMs","created":"2025-05-27","cited_by_count":3,"area":null},{"id":"2604.10689","title":"Communication-Efficient Gluon in Federated Learning","created":"2026-04-12","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":"2402.11857","title":"Communication-Efficient Distributed Learning with Local Immediate Error Compensation","created":"2024-02-19","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":"2408.13787","title":"Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning","created":"2024-08-25","cited_by_count":2,"area":null},{"id":"2412.16414","title":"Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity","created":"2024-12-21","cited_by_count":2,"area":null},{"id":"2510.03507","title":"Composite Optimization with Error Feedback: the Dual Averaging Approach","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2511.14501","title":"Improved Convergence in Parameter-Agnostic Error Feedback through Momentum","created":"2025-11-18","cited_by_count":2,"area":null},{"id":"2402.03770","title":"Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes","created":"2024-02-06","cited_by_count":1,"area":null},{"id":"2604.24012","title":"FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection","created":"2026-04-27","cited_by_count":1,"area":null},{"id":"2410.23424","title":"Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching","created":"2024-10-30","cited_by_count":0,"area":null},{"id":"2602.15996","title":"Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2607.01755","title":"Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization","created":"2026-07-02","cited_by_count":0,"area":null}],"next_cursor":null}