FedGPS: Personalized Cross-Silo Federated Learning for Internet of Things-enabled Predictive Maintenance

Abstract

Predictive maintenance (PdM) has entered into a new era adopting artificial intelligence and Internet-of-Things (IoT) technologies. It is necessary for a manufacturing company to collaborate with other clients using IoT-captured production data. However, training models in a cross-silo manner is still challenging when considering data privacy. In order to tackle these challenges, a personalized cross-silo federated learning mechanism named federated global partners searching (FedGPS) is proposed. Firstly, model parameters for the participating clients are encrypted and uploaded to the central server as input. Next, FedGPS automatically determines the collaboration degrees between clients based on data distribution. After that, personalized model updates are sent back to the clients. Finally, each client conducts local updating after data decryption. The effectiveness of the FedGPS is verified in real-world cases and our method achieves 92.35% Accuracy, 98.55% Precision, 92.90% Recall, and 95.27% F1-Score comparing with other existing models from the literature.