PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
This is an incremental improvement for federated learning practitioners dealing with heterogeneous data distributions and communication constraints.
The paper tackles the problem of data heterogeneity and high communication costs in federated learning by proposing PS-FedGAN, a framework that uses partially shared generative adversarial networks, which reduces communication overhead by 30-50% while maintaining privacy and improving model performance on non-IID data.
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.