HfedPES: Hierarchical Personalized Federated Learning with Edge Selection

Federated learning may protect user privacy, reduce the transmission of a large amount of raw data, and is more compatible with smart home applications. Current federated learning faces two major problems including non-independent and identically (Non-IID) distributed data and high communication overhead. Personalized federated learning is a good method to deal with Non-IID data, but current personalized federated learning methods overlook the shared features of users' living habits in the same region. Hierarchical federated learning can reduce traffic on the core network, but its potential for personalization for smart home applications has not been considered. Therefore, to address these issues simultaneously, we propose hierarchical personalized federated learning. Specifically, we adopt a three-layer federated learning architecture of cloud-edge-client. On this basis, we use differential learning classification loss (DLCL), hierarchical balance loss (HBL) and balanced edge data selection (BEDS) methods to achieve the personalization of models on both the device side and the edge side. Finally, our experiments demonstrate that compared to state-of-the-art federated learning methods, hierarchical personalized federated learning has improvements in model accuracy and communication overhead.