Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

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.

9 pages

References in corpus (5)

• FedKD: Communication Efficient Federated Learning via Knowledge Distillation
• Grad-CAM: Why did you say that?
• Overcoming Forgetting in Federated Learning on Non-IID Data
• End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
• Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system