Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks

2025-06-20

AI Summary

In bandwidth-constrained, heterogeneous IoT networks, federated learning (FL) faces significant challenges in client selection due to non-IID data distributions, varying data volumes, and heterogeneous device resources. To address this, this paper pioneers the integration of semantic communication with FL, proposing a *federated semantic communication* framework. It employs an end-to-end semantic encoding–reconstruction architecture with a semantic bottleneck layer, transmitting only task-critical semantic features—thereby preserving image reconstruction fidelity while enhancing privacy. We design a loss-based adaptive model aggregation mechanism and propose three dynamic client selection strategies—Utilitarian, Proportional Fairness, and others—balancing reconstruction performance, fairness, and resource efficiency. Experiments demonstrate that the Utilitarian strategy achieves optimal reconstruction quality; meanwhile, the Proportional Fairness strategy maintains competitive performance while reducing participation inequality by 37% and improving edge computing efficiency by 2.1×.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Efficient data transmission in bandwidth-constrained IoT networks
• Client selection in federated learning with heterogeneous devices
• Balancing reconstruction quality, resource efficiency, and fairness

Innovation

• Federated semantic communication reduces bandwidth usage
• Client selection strategies balance performance and fairness
• Loss-based aggregation adapts to client heterogeneity