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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

Summary

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 computing federated learning stochastic client selection topology optimization wireless networks

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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.