  Federated Learning (FL) has emerged as an effective learning paradigm for
distributed computation owing to its strong potential in capturing underlying
data statistics while preserving data privacy. However, in cases of practical
data heterogeneity among FL clients, existing FL frameworks still exhibit
deficiency in capturing the overall feature properties of local client data
that exhibit disparate distributions. In response, generative adversarial
networks (GANs) have recently been exploited in FL to address data
heterogeneity since GANs can be integrated for data regeneration without
exposing original raw data. Despite some successes, existing GAN-related FL
frameworks often incur heavy communication cost and also elicit other privacy
concerns, which limit their applications in real scenarios. To this end, this
work proposes a novel FL framework that requires only partial GAN model
sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and
training mechanism to address heterogeneous data distributions across clients
and to strengthen privacy preservation at reduced communication cost,
especially over wireless networks. Our analysis demonstrates the convergence
and privacy benefits of the proposed PS-FEdGAN framework. Through experimental
results based on several well-known benchmark datasets, our proposed PS-FedGAN
shows great promise to tackle FL under non-IID client data distributions, while
securing data privacy and lowering communication overhead.
