Energy-Efficient Personalized Federated Continual Learning on Edge

Abstract

Federated learning (FL) on the edge devices must support continual learning (CL) to handle continuously evolving the data and perform the model training in an energy-efficient manner to accommodate the devices with limited computational and energy resources. This letter proposes an energy-efficient personalized federated CL (FCL) framework for the edge devices. The network structure on each device is divided into parts for retaining old knowledge and learning new knowledge, training only part of the model to reduce overhead. A data-free parameter selection approach selects important parameters from the trained model to retain old knowledge. During new task learning, a federated search method determines a resource-adaptive personalized model structure for each device. Experimental results demonstrate that our method can effectively support FCL in an energy-efficient manner on the edge devices.