Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
This addresses data privacy, model adaptation, and explainability for fire surveillance in industrial networks, but it appears incremental as it builds on existing semantic and federated learning techniques.
The paper tackles the problem of high spectrum resource consumption in fire surveillance by Industrial Internet of Things devices by proposing an Industrial Edge Semantic Network that uses semantic communication to send warnings, with simulation results showing effectiveness.
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.