mlsanityData to 7 Oct 2026

Federated Learning with Flexible Control

arXiv 2212.08496 ↗First posted 16 Dec 2022cs.LG cs.DC cs.ITcited by 12

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g., mobile edge networks, the efficiency of FL is an important problem. Existing works have separately considered different configurations to make FL more efficient, such as infrequent transmission of model updates, client subsampling, and compression of update vectors. However, an important open problem is how to jointly apply and tune these control knobs in a single FL algorithm, to achieve the best performance by allowing a high degree of freedom in control decisions. In this paper, we address this problem and propose FlexFL - an FL algorithm with multiple options that can be adjusted flexibly. Our FlexFL algorithm allows both arbitrary rates of local computation at clients and arbitrary amounts of communication between clients and the server, making both the computation and communication resource consumption adjustable. We prove a convergence upper bound of this algorithm. Based on this result, we further propose a stochastic optimization formulation and algorithm to determine the control decisions that (approximately) minimize the convergence bound, while conforming to constraints related to resource consumption. The advantage of our approach is also verified using experiments.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 38 of its 51 references that are in the map. First posted on arXiv 16 Dec 2022.

Builds on

38 of 51 references.

References · 38

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  7. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  8. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2019Federated Machine Learning: Concept and Applicationscited by 409
  11. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  12. 2018Sparsified SGD with Memorycited by 373
26 more
  1. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  2. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  3. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 306
  4. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  5. 2018The Convergence of Sparsified Gradient Methodscited by 228
  6. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 201
  7. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 199
  8. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  9. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 179
  10. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 159
  11. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 106
  12. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  13. 2019DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compressioncited by 98
  14. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  15. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  16. 2020Local SGD: Unified Theory and New Efficient Methodscited by 88
  17. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  18. 2021MARINA: Faster Non-Convex Distributed Learning with Compressioncited by 77
  19. 2018Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDcited by 76
  20. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  21. 2020Cost-Effective Federated Learning Designcited by 61
  22. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 45
  23. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  24. 2020Optimal Gradient Compression for Distributed and Federated Learningcited by 37
  25. 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
  26. 2021Optimal Rate Adaption in Federated Learning with Compressed Communicationscited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 2.

12 papers.

  1. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  2. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  3. 2024Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translationcited by 2
  4. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  5. 2024Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  6. 2025Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimizationcited by 1
  7. 2023Timely Asynchronous Hierarchical Federated Learning: Age of Convergencecited by 0
  8. 2024Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learningcited by 0
  9. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0
  10. 2025TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correctioncited by 0
  11. 2026FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learningcited by 0
  12. 2026Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problemcited by 0