Open Access

ARTICLE

FedCW: Client Selection with Adaptive Weight in Heterogeneous Federated Learning

Haotian Wu¹, Jiaming Pei², Jinhai Li^3,*

1 School of Computer Science, Torrens University Australia, Sydney, NSW 2007, Australia
2 School of Computer Science, The University of Sydney, Camperdown, Sydney, NSW 2006, Australia
3 College of Economics and Management, Taizhou University, Taizhou, 225300, China

* Corresponding Author: Jinhai Li. Email:

(This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)

Computers, Materials & Continua 2026, 86(1), 1-20. https://doi.org/10.32604/cmc.2025.069873

Received 02 July 2025; Accepted 29 September 2025; Issue published 10 November 2025

Abstract

With the increasing complexity of vehicular networks and the proliferation of connected vehicles, Federated Learning (FL) has emerged as a critical framework for decentralized model training while preserving data privacy. However, efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging. To address these issues, we propose Federated Learning with Client Selection and Adaptive Weighting (FedCW), a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks. FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence. Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg, FedProx, and SCAFFOLD, particularly in non-IID settings, achieving faster convergence, higher accuracy, and reduced communication overhead. These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous, edge-based computing environments.

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