PFL-GAN: Client Heterogeneity Meets Generative Models in Personalized Federated Learning

Recent advances in generative artificial intelligence (AI) have led to rising interest in federated learning (FL) based on generative adversarial network (GAN) models. GAN-based FL shows promises in many communication and network applications, such as edge computing and the Internet of Things. In the context of FL, GANs can capture the underlying client data structure, and regenerate samples resembling the original data distribution without compromising data privacy. Although most existing GAN-based FL works focus on training a global model, some scenarios exist where personalized FL (PFL) can be more desirable when incorporating client data heterogeneity in terms of distinct data distributions, feature spaces, and labels. To cope with client heterogeneity in GAN-based FL, we propose a novel GAN sharing and aggregation strategy for PFL that can efficiently characterize client heterogeneity in different settings. More specifically, our proposed PFL-GAN first learns the similarities among clients before implementing a weighted collaborative data aggregation. Our empirical results through rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.