Personalized Federated Transfer Learning for Cycle-Life Prediction of Lithium-Ion Batteries in Heterogeneous Clients With Data Privacy Protection

Health prognostics within the Internet of Things (IoT) paradigm face several challenges, including data privacy, client drift, and prediction accuracy. Federated learning (FL), as an emerging decentralized machine learning paradigm, has the potential to address these challenges by integrating multiple data silos in a distributed and privacy-preserved fashion. This article develops a novel personalized federated transfer learning (PFTL) framework for customized health prognosis of multiple heterogeneous clients. The framework starts with a powerful initial global prognostic model that is pretrained using a publicly available data set in a central server. The pretrained global model is then distributed to the local clients and fine-tuned separately on their respective private data sets. The fine-tuned local prognostic models are uploaded to the central server for dynamic weighted model aggregation. The aggregated model is then distributed to each client for implementing domain adversarial training to obtain a fine-grained local prognostic model. The proposed PFTL framework embeds a multiscale attention module and a multihead self-attention module parallelly into the deep learning-based prognostic model, which is shared between the central server and each local client. Through experimental verifications from lab testing-based and open-source fast-charging lithium-ion batteries data sets, we demonstrate that the proposed method can achieve accurate cycle-life prediction without compromising data privacy.