Hybrid machine learning framework with data analytics model for privacy-preserved intelligent predictive maintenance in healthcare iot

HIGHLIGHTS

• What: In light of these challenges, this work proposes a robust solution for privacy-preserved intelligent predictive maintenance in healthcare IoT. The authors propose a hybrid framework that integrates Federated Learning and Adaptive Moving Window Regression to adaptively determine server step sizes in FL Privacy preservation: The study aimed to improve healthcare data processing using IoT and machine_learning techniques. The study aimed to confirm the statistical significance of improvements in performance metrics such as accuracy, precision, recall, F1-score, and computational time between the FedDyn AMWR method and comparison methods (FRESH, FL-BETS, and VFL).
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