Abstract

The rapid integration of wireless sensor networks in healthcare monitoring has created strong opportunities for continuous patient assessment. However, the distributed nature of these networks has exposed sensitive medical data to significant privacy and security risks. Traditional centralized learning models have struggled to protect patient information, particularly when the data has/have been transmitted across heterogeneous devices. This study addressed these concerns by evaluating an enhanced secure federated learning framework that has/have reduced communication overhead and strengthened protection against model-level threats. The problem emerged when conventional federated models failed to defend aggregated parameters against inference attacks that targeted the intermediates shared during training. To overcome this limitation, the proposed system integrated authenticated encryption, differential privacy, and a lightweight blockchain layer that/which supported tamper-proof logging. The method followed a three-stage design that/which included secure client selection, privacy-preserved gradient update, and decentralized model validation. The wireless nodes operated with an adaptive update schedule that/which minimized energy use while maintaining stable model convergence. The evaluation demonstrates that the proposed secure federated learning framework achieves a classification accuracy of 96.0%, outperforming Encrypted Aggregation FL (93.0%), Differential Privacy FL (90.2%), and Blockchain-Assisted FL (94.2%). The communication cost has/have been reduced to 17.2 MB from 22.0 MB, 18.1 MB, and 23.5 MB, respectively. Energy consumption per node is lowered to 1.95 J, compared to 2.45 J, 2.68 J, and 2.63 J in the existing methods. The system achieves a privacy preservation score of 0.94, higher than 0.75– 0.87 in baseline approaches, and maintains strong model robustness at 94.2% under adversarial conditions. These results validate that the proposed framework provides reliable, energy-efficient, and secure federated learning suitable for real-time healthcare monitoring applications.