Federated Learning Enabled Wireless Sensor Architecture for Secure and Intelligent Brain Surgery Monitoring

Abstract

Real-time physiological monitoring during brain surgery is critical for ensuring intraoperative safety and precision. Traditional centralized artificial intelligence (AI) models, although powerful, pose challenges related to data privacy, latency, and network reliability—factors that are particularly sensitive in neurosurgical environments. This paper proposes a novel architecture that integrates federated learning with wireless sensor networks (WSNs) to enable secure, decentralized, and intelligent intraoperative monitoring. In the proposed system, distributed sensor nodes equipped with local AI models perform real-time analysis of neurophysiological signals such as electroencephalography (EEG) and intracranial pressure (ICP). Model updates are shared instead of raw data, preserving patient privacy while enabling collaborative learning through federated averaging. Experimental simulations demonstrate that the federated learning approach achieves comparable prediction accuracy to centralized models while significantly reducing communication overhead and enhancing data security. The architecture also supports scalability, resilience to single-point failures, and adaptability across varied surgical contexts. This study lays the groundwork for deploying privacy-preserving AI systems in high-stakes surgical procedures and paves the way for intelligent, edge-enabled brain-computer interfaces.