mlsanityData to 14 Oct 2026

A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret

arXiv 2301.08869 ↗First posted 21 Jan 2023cs.LG cs.DCcited by 7

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Abstract (arXiv)

We consider the problem of online stochastic optimization in a distributed setting with M clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon. This is in contrast to existing studies which focus on the offline measure of simple regret for learning efficiency. The holistic measure for communication cost also departs from the prevailing approach that separately tackles the communication frequency and the number of bits in each communication round.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 21 Jan 2023.

Builds on

36 of 53 references.

References · 36

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2020Adaptive Federated Optimizationcited by 1,046
  5. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  6. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  7. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  8. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  9. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  10. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 180
  11. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  12. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
24 more
  1. 2020Is Local SGD Better than Minibatch SGD?cited by 151
  2. 2020Minibatch vs Local SGD for Heterogeneous Distributed Learningcited by 147
  3. 2022ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!cited by 134
  4. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  5. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 107
  6. 2019A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Normcited by 106
  7. 2019DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compressioncited by 99
  8. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 63
  9. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  10. 2020Faster Non-Convex Federated Learning via Global and Local Momentumcited by 48
  11. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 41
  12. 2022BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compressioncited by 41
  13. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  14. 2022SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compressioncited by 34
  15. 2019Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradientscited by 32
  16. 2020Communication-Efficient Distributed Deep Learning: A Comprehensive Surveycited by 26
  17. 2022Towards Efficient Communications in Federated Learning: A Contemporary Surveycited by 24
  18. 2019Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learningcited by 18
  19. 2020Local SGD With a Communication Overhead Depending Only on the Number of Workerscited by 13
  20. 2019Exploring Fast and Communication-Efficient Algorithms in Large-scale Distributed Networkscited by 8
  21. 2022Distributed Linear Bandits under Communication Constraintscited by 8
  22. 2019Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Datacited by 7
  23. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6
  24. 2022A-LAQ: Adaptive Lazily Aggregated Quantized Gradientcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 1.

7 papers.

  1. 2024Locally Differentially Private Online Federated Learning With Correlated Noisecited by 6
  2. 2023Sparse Training for Federated Learning with Regularized Error Correctioncited by 2
  3. 2025Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learningcited by 2
  4. 2025Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimizationcited by 1
  5. 2023A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systemscited by 0
  6. 2025Federated Reinforcement Learning in Heterogeneous Environmentscited by 0
  7. 2026Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networkscited by 0