Predictive Maintenance using Federated Learning with Edge-Based Anomaly Detection

Abstract

Predictive maintenance aims to anticipate equipment failures, minimizing downtime and maintenance costs. Traditional approaches often rely on centralized data collection, raising significant privacy concerns, particularly when dealing with sensitive sensor data from industrial equipment. This research proposes a novel framework that leverages the strengths of federated learning and edge-based anomaly detection to achieve robust and privacy-preserving predictive maintenance. The core idea is to train a predictive maintenance model collaboratively across multiple devices using federated learning, while simultaneously employing local anomaly detection models residing on edge devices to identify deviations from normal operation *before* data transmission. This approach reduces the amount of raw data sent to a central server, thereby mitigating privacy risks and potentially improving model accuracy through localized anomaly detection. The framework is designed to be scalable and adaptable to various industrial environments. We present a detailed discussion of the system architecture, the federated learning process, and the edge-based anomaly detection techniques. The proposed method offers a compelling solution for industries seeking to proactively manage equipment health while safeguarding sensitive data.