mlsanityData to 14 Oct 2026

Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression

arXiv 2111.13931 ↗First posted 27 Nov 2021cs.LG cs.DC cs.MAcited by 5

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Abstract (arXiv)

Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able to operate reliably. Besides varying amounts of non-IID data at participants, the asynchronous setting models heterogeneous client participation due to available computational power and battery constraints and also accounts for delayed communications between clients and the server. To reduce the communication overhead associated with asynchronous online federated learning (ASO-Fed), we use the principles of partial-sharing-based communication. In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible. We prove the convergence of the proposed ASO-Fed and provide simulations to analyze its behavior further. The simulations reveal that, in the asynchronous setting, it is possible to achieve the same convergence as the federated stochastic gradient (Online-FedSGD) while reducing the communication tenfold.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 27 Nov 2021.

Builds on

16 of 26 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  5. 2018Federated Learning with Non-IID Datacited by 1,108
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  8. 2019Asynchronous Federated Optimizationcited by 267
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 100
  11. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
  12. 2020Age of Information: An Introduction and Surveycited by 70
4 more
  1. 2020FedADC: Accelerated Federated Learning with Drift Controlcited by 26
  2. 2021Communication-Efficient Online Federated Learning Framework for Nonlinear Regressioncited by 16
  3. 2021Asynchronous Federated Learning for Sensor Data with Concept Driftcited by 14
  4. 2016Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspectivecited by 2

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Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  2. 2023Federated Online and Bandit Convex Optimizationcited by 7
  3. 2023Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimizationcited by 3
  4. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  5. 2023Asynchronous Online Federated Learning with Reduced Communication Requirementscited by 2