Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

2024-12-01

IEEE Transactions on Industrial Informatics

AI Summary

To address spectrum waste, privacy leakage, device heterogeneity, and opaque decision-making in industrial intelligent monitoring networks with high-frequency fire alarm data transmission, this paper proposes an explainable semantic federated edge network for fire monitoring. The method introduces the first Explainable Semantic Federated Learning (XSFL) framework, integrating Fisher information matrix–driven client-adaptive training, leakyReLU activation mapping–guided explainable semantic-data relationship modeling, and lightweight semantic communication for compressed alert transmission. This approach significantly reduces communication overhead, preserves end-device data privacy, enhances semantic modeling accuracy across heterogeneous IIoT devices, and improves decision interpretability. Simulation results demonstrate superior performance in spectral efficiency, model generalization, and explanation fidelity compared to baseline methods.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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 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 strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC 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.

Problem

• Industrial Intelligent Surveillance Networks
• Wireless Signal Resource Management
• Data Security and Comprehensibility

Innovation

• Semantic Federated Learning
• Adaptive Training Method (ACT)
• Explainable Secure Communication (ESC)