mlsanityData to 14 Oct 2026

Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival

arXiv 2408.05886 ↗First posted 12 Aug 2024cs.LG cs.DC cs.NIcited by 1

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Abstract (arXiv)

Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL). Although the limited (radio and computational) resources are widely acknowledged, two critical yet often ignored aspects are (a) client devices can only dedicate a small chunk of their limited storage for the FL task and (b) new training samples may arrive continually in many practical wireless applications. Therefore, we propose a new FL algorithm, online-score-aided federated learning (OSAFL), specifically designed for tasks with continual data arrival in resource-constrained environments. We first theoretically show how the convergence bound is affected by continual data distribution shifts, uncertain client participation, gradient quantization errors, and noise from stochastic gradients and statistical data heterogeneity across clients. We then show how to (sub-optimally) minimize these errors by choosing appropriate aggregation weights at the CS during global update. Our extensive simulation results across three popular image classification datasets and three ML models with different numbers of trainable parameters validate the effectiveness of the proposed OSAFL algorithm compared to (modified) state-of-the art FL baselines.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 12 Aug 2024.

Builds on

27 of 47 references.

References · 27

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  12. 2019Federated Learning over Wireless Fading Channelscited by 183
15 more
  1. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  2. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
  3. 2023Better Generative Replay for Continual Federated Learningcited by 55
  4. 2021Anarchic Federated Learningcited by 42
  5. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  6. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  7. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  8. 2022Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantizationcited by 14
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  10. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  11. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
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  13. 2024Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networkscited by 4
  14. 2022Resource Consumption for Supporting Federated Learning in Wireless Networkscited by 3
  15. 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learningcited by 3

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Citing papers per year: 2025: 1.

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