Balancing communication efficiency and model performance in federated learning using sparse models
Mohd Ahmed1 and Rajendra Kumar1
Department of Computer Science,Jamia Millia Islamia,New Delhi-110025,India1
Corresponding Author : Mohd Ahmed
Recieved : 29-March-2025; Revised : 20-March-2026; Accepted : 24-March-2026
Abstract
Federated learning (FL) faces significant challenges in balancing communication efficiency and model performance, particularly when deploying complex architectures such as residual network (ResNet-18) on heterogeneous, real-world datasets like the Canadian Institute for Advanced Research (CIFAR-10). This paper investigates the combined effects of model complexity, dataset difficulty, and non-independent and identically distributed (non-IID) data distributions on the sparsified FL method, FedSparseT. It provides a comprehensive analysis of the role of sparsity in reducing communication overhead while maintaining model accuracy. Through systematic experimentation across varying sparsity levels (0.025–0.4), the trade-offs among compression efficiency, convergence stability, and final model performance are evaluated in resource-constrained, non-IID environments. The results demonstrate that higher sparsity levels reduce communication costs by up to 17% (achieving a transmitted size of 70.47 KB compared to 84.76 KB at 2.5% sparsity) while also delivering improved final accuracy (0.52 at the 200th round). Furthermore, it is observed that the architectural complexity of ResNet-18, combined with the fine-grained classification requirements of CIFAR-10, creates a highly challenging scenario under non-IID conditions. In such settings, sparsification may inadvertently prune critical parameters, thereby slowing feature learning, as evidenced by accuracy below 0.2 during early training rounds. Although sparsity functions as an implicit regularization mechanism (final loss of 2.12 for 4% sparsity versus 2.23 for 2.5%), its effectiveness is limited using fixed thresholds that fail to adapt to evolving model requirements and client heterogeneity.
Keywords
Federated learning (FL), Sparsification, Non-IID data distribution, Communication efficiency, ResNet-18, CIFAR-10 dataset.
Cite this article
Ahmed M, Kumar R. Balancing communication efficiency and model performance in federated learning using sparse models. International Journal of Advanced Technology and Engineering Exploration. 2026;13(136):393-409. DOI : 10.19101/IJATEE.2025.121220419
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