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Related Experiment Videos

TCS-FEEL: Topology-Optimized Federated Edge Learning with Client Selection.

Hui Chen1, He Li1

  • 1Department of Sciences and Informatics, Muroran Institute of Technology, Muroran 050-0071, Hokkaido, Japan.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

TCS-FEEL optimizes federated learning (FL) by using topology-aware client selection. This framework improves privacy-preserving, resource-efficient edge intelligence in dynamic networks.

Keywords:
edge computingfederated learningstochastic client selectiontopology optimizationwireless networks

Related Experiment Videos

Area of Science:

  • Edge Computing
  • Machine Learning
  • Wireless Networks

Background:

  • Federated learning (FL) enables privacy-preserving distributed training on edge devices.
  • Statistical and system heterogeneity in wireless networks hinder FL performance.
  • Existing FL methods struggle with dynamic environments and efficient resource use.

Purpose of the Study:

  • To propose TCS-FEEL, a topology-aware client selection framework for federated learning.
  • To address challenges of statistical heterogeneity and system dynamics in edge environments.
  • To enhance privacy-preserving and resource-efficient FL through optimized client selection and communication.

Main Methods:

  • Developed TCS-FEEL, integrating user distribution, device-to-device (D2D) communication, and data statistical similarity for client selection.
  • Implemented an adaptive tree-based communication structure with randomized client sampling.
  • Utilized edge devices as relays to leverage D2D transmission for efficient model aggregation.

Main Results:

  • TCS-FEEL significantly reduced the number of training rounds and per-round wall-clock time.
  • The framework maintained high model accuracy across various non-IID data distributions and mobility settings.
  • Demonstrated superior performance compared to existing baselines in extensive experiments on MNIST and CIFAR-10.

Conclusions:

  • Integrating topology control with client selection effectively accelerates federated learning.
  • TCS-FEEL offers a robust solution for privacy-preserving and resource-efficient FL in sensor-rich, dynamic edge environments.
  • The approach is well-suited for applications like autonomous driving, smart city monitoring, and Industrial IoT.