Federated Learning Framework for Collaborative Time Series Anomaly Detection on Distributed Machines
Ignatius Iwan, Tori Andika Bukit, Bernardo Nugroho Yahya, Seok‐Lyong Lee · 2024
Detecting an anomaly is an essential task in the manufacturing operation. Due to the vast adaptation of machines for industry, AI has become an indispensable part of detecting anomalous instances. However, data scarcity and cost allocation pose a significant challenge for individual companies to train a model alone. Therefore, in Industries 5.0, companies need to collaborate to achieve the common goal. On the other hand, they also need to disclose sensitive information according to General Data Protection Regulation (GDPR). This work presents a secure collaborative framework with Federated Learning to enable the development of an anomaly detection model among multiple machines or clients in different companies. The proposed framework performs tasks such as managing secure connections among clients, transforming client data to a processable format, and conducting model training between clients simultaneously.