Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

TL;DR

This paper introduces an explainable federated learning framework for semantic communication in industrial IoT fire surveillance, addressing privacy, heterogeneity, and interpretability challenges with effective simulation results.

Contribution

It proposes a novel eXplainable Semantic Federated Learning (XSFL) framework with adaptive training and explainability mechanisms for industrial edge networks.

Findings

XSFL effectively trains semantic models with privacy preservation.

Adaptive client training improves model performance for heterogeneous devices.

Explainability mechanism clarifies semantics-to-data relationships.

Abstract

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL.