mlsanityData to 7 Oct 2026

FedGiA: An Efficient Hybrid Algorithm for Federated Learning

arXiv 2205.01438 ↗First posted 3 May 2022cs.LG math.OCcited by 8

Not on the map. Why?

Abstract (arXiv)

Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge. To address these critical issues, we propose a hybrid federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direction method of multipliers. The proposed algorithm is more communication- and computation-efficient than several state-of-the-art algorithms theoretically and numerically. Moreover, it also converges globally under mild conditions.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 34 of its 61 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

34 of 61 references.

References · 34

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  10. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  11. 2018Federated Learning via Over-the-Air Computationcited by 215
  12. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 201
22 more
  1. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  2. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
  3. 2016Asynchronous Stochastic Gradient Descent with Delay Compensationcited by 98
  4. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
  5. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 91
  6. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  7. 2020Federated Learning in Vehicular Networkscited by 53
  8. 2017Mobile Edge Computing: A Survey on Architecture and Computation Offloadingcited by 52
  9. 2022Federated Learning via Inexact ADMMcited by 44
  10. 2018DP-ADMM: ADMM-based Distributed Learning with Differential Privacycited by 41
  11. 2020Federated Learning and Wireless Communicationscited by 27
  12. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 27
  13. 2020Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learningcited by 22
  14. 2018Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithmscited by 20
  15. 2020Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theorycited by 16
  16. 2021Differentially Private Federated Learning via Inexact ADMMcited by 11
  17. 2020Federated Nonconvex Sparse Learningcited by 10
  18. 2021Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Cachingcited by 10
  19. 2019L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learningcited by 8
  20. 2018Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithmscited by 7
  21. 2020Decentralized Learning with Lazy and Approximate Dual Gradientscited by 6
  22. 2017Robust Decentralized Learning Using ADMM with Unreliable Agentscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 1; 2026: 2.

8 papers.

  1. 2023Federated Reinforcement Learning for Resource Allocation in V2X Networkscited by 3
  2. 2023Sparse Decentralized Federated Learningcited by 2
  3. 2025Preconditioned Inexact Stochastic ADMM for Deep Modelcited by 2
  4. 2023Distributed Variational Inference for Online Supervised Learningcited by 1
  5. 2026Decentralized Federated Learning by Partial Message Exchangecited by 1
  6. 2023Personalized Federated Learning via ADMM with Moreau Envelopecited by 0
  7. 2024On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairnesscited by 0
  8. 2026Event-Triggered Gossip for Distributed Learningcited by 0