PFL-GAN: When Client Heterogeneity Meets Generative Models in
  Personalized Federated Learning
Achintha Wijesinghe Songyang Zhang Zhi Ding
Recent advances of generative learning models are accompanied by the growing
interest in federated learning (FL) based on generative adversarial network
(GAN) models. In the context of FL, GAN can capture the underlying client data
structure, and regenerate samples resembling the original data distribution
without compromising the private raw data. Although most existing GAN-based FL
works focus on training a global model, Personalized FL (PFL) sometimes can be
more effective in view of client data heterogeneity in terms of distinct data
sample 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. The proposed PFL-GAN addresses the client heterogeneity in
different scenarios. More specially, we first learn the similarity among
clients and then develop an weighted collaborative data aggregation. The
empirical results through the rigorous experimentation on several well-known
datasets demonstrate the effectiveness of PFL-GAN.