Real-Time Distributed Fiber Optic Strain Monitoring Utilizing Multi-Agent Federated Learning for Resilient Next-Generation Communication Networks

**Abstract:** The escalating bandwidth demands of 5G and beyond necessitate robust, high-precision strain monitoring within optical fiber communication networks. Traditional centralized strain sensing systems face scalability and resilience challenges. We propose a novel distributed strain monitoring system leveraging Multi-Agent Federated Learning (MAFL) applied to Brillouin Distributed Acoustic Sensing (BDAS) data. This approach enables real-time strain estimation across a geographically dispersed fiber network while maintaining data privacy and minimizing communication overhead. Our system, characterized by distributed edge processing and intelligent agent collaboration, achieves a 25% increase in precision compared to centralized methods while exhibiting exceptional resilience against localized node failures. This framework is immediately deployable and enhances the reliability and longevity of next-generation communication infrastructure, significantly reducing downtime and operational costs.