mlsanityData to 14 Oct 2026

Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks

arXiv 2311.04350 ↗First posted 7 Nov 2023cs.NI cs.DC cs.LGcited by 2

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Abstract (arXiv)

The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, and (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using these results, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and D2D data offloading to maximize FedL accuracy. Through evaluation on popular datasets and real-world network measurements from our edge testbed, we find that our methodology outperforms popular device sampling methodologies from literature in terms of ML model performance, data processing overhead, and energy consumption.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 89 references that are in the map. First posted on arXiv 7 Nov 2023.

Builds on

35 of 89 references.

References · 35

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23 more
  1. 2019Federated Learning over Wireless Fading Channelscited by 183
  2. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  3. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  4. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  5. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  6. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
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  8. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  9. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  10. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  11. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  12. 2020Network-Aware Optimization of Distributed Learning for Fog Computingcited by 27
  13. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
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  15. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  16. 2020Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learningcited by 16
  17. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  18. 2021Relay-Assisted Cooperative Federated Learningcited by 9
  19. 2022Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholdscited by 9
  20. 2023Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networkscited by 8
  21. 2023Multi-Source to Multi-Target Decentralized Federated Domain Adaptationcited by 6
  22. 2022Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchangecited by 5
  23. 2023Five Common Misconceptions About Privacy-Preserving Internet of Thingscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 11
  2. 2026Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizerscited by 0