Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks

TL;DR

This paper introduces a federated semantic communication framework for IoT networks that reduces bandwidth usage and preserves privacy by transmitting semantic features, with client selection strategies balancing performance and fairness.

Contribution

It proposes a novel federated semantic communication architecture with three client selection strategies tailored for heterogeneous IoT devices, addressing efficiency and fairness challenges.

Findings

Utilitarian selection yields highest image reconstruction quality.

Proportional fairness reduces participation inequality.

Framework balances reconstruction quality, resource efficiency, and fairness.

Abstract

The exponential growth of IoT devices presents critical challenges in bandwidth-constrained wireless networks, particularly regarding efficient data transmission and privacy preservation. This paper presents a novel federated semantic communication (SC) framework that enables collaborative training of bandwidth-efficient models for image reconstruction across heterogeneous IoT devices. By leveraging SC principles to transmit only semantic features, our approach dramatically reduces communication overhead while preserving reconstruction quality. We address the fundamental challenge of client selection in federated learning environments where devices exhibit significant disparities in dataset sizes and data distributions. Our framework implements three distinct client selection strategies that explore different trade-offs between system performance and fairness in resource allocation. The system employs an end-to-end SC architecture with semantic bottlenecks, coupled with a loss-based aggregation mechanism that naturally adapts to client heterogeneity. Experimental evaluation on image data demonstrates that while Utilitarian selection achieves the highest reconstruction quality, Proportional Fairness maintains competitive performance while significantly reducing participation inequality and improving computational efficiency. These results establish that federated SC can successfully balance reconstruction quality, resource efficiency, and fairness in heterogeneous IoT deployments, paving the way for sustainable and privacy-preserving edge intelligence applications.