Domain-Adaptive Clustered Federated Transfer Learning for EV Charging Demand Forecasting

To address the privacy concerns for state-of-the-art cutting-edge centralized machine learning in electric vehicle (EV) charging demand forecasting applications, federated learning (FL) has been employed to transfer training processes from the cloud server to edge devices. Nevertheless, traditional FL still grapples with several challenges in terms of personalization, transferability, feature extraction, and data security. This study proposes a domain-adaptive clustered federated transfer learning (FTL) scheme for EV charging demand forecasting. This scheme combines the principles of transfer learning (TL) with FL by utilizing maximum mean discrepancy to measure the differences between local features and cluster them, weight local model parameters in the global model aggregation, and realize domain adaptation for projecting local data and new data to the trained FL model. A multi-head attention-based transformer is leveraged to construct a forecasting model to focus on the most relevant spatio-temporal features. Under multi-stage differential privacy protections, Laplace noise is injected into the local feature, model update and local model during the clustering, FL and TL processes, respectively. The case study demonstrates that the proposed domain-adaptive clustered FTL outperforms the conventional FTL and FL, local training, and other domain shift handling techniques in predictive accuracy and operational risk.