A Novel Federated Learning Framework Based on Conditional Generative Adversarial Networks for Privacy Preserving in 6G

Abstract

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN.