Privacy-Preserving Federated Spiking Neural Networks for Real-Time Target Detection in Distributed ISAC Edge Systems

This study proposes a privacy-preserving federated spiking neural network (SNN) framework for real-time target detection in integrated sensing and communication (ISAC) edge networks. The framework enables distributed vehicular nodes operating at 28 GHz to collaboratively learn from spike-based sensing data without sharing raw observations, thereby protecting sensitive location information. By combining federated learning with secure model aggregation, adaptive differential privacy, and spike-aware temporal learning, the system ensures robust privacy protection while supporting efficient learning under non-IID data and real-time constraints. Extensive experiments using data from 500 vehicles and 50,000 observations demonstrate that the proposed approach achieves a high detection performance of 94.3% F1-score under strict privacy guarantees, with low inference latency and significantly reduced energy consumption. These results highlight the framework’s suitability for privacy-sensitive vehicular and smart-city applications, supporting scalable, energy-efficient, and trustworthy 6G ISAC deployment.