FedGen: Personalized federated learning with data generation for enhanced model customization and class imbalance

Abstract

Federated learning has emerged as a prominent solution for the collaborative training of machine learning models without exchanging local data. However, existing approaches often impose rigid constraints on model heterogeneity, limiting the ability of clients to customize unique models and increasing the vulnerability of models to potential attacks. This paper presents FedGen, a novel personalized federated learning framework based on generative adversarial networks (GANs). FedGen shifts the focus from training task-specific models to generating data, especially for minority classes with imbalanced data. With FedGen, clients can gain knowledge from others by training generators, while maintaining a heterogeneous local model and avoiding sharing model information with other participants. Moreover, to address challenges arising from imbalanced data, we propose AT-GAN, a novel generative model incorporating pseudo augmentation and differentiable augmentation modules to foster healthy competition between the generator and discriminator. To evaluate the effectiveness of our approach, we conduct extensive experiments on real-world tabular datasets. The experimental results demonstrate that FedGen significantly enhances the performance of local models, achieving improvements of up to 11.92% in F1 score and up to 9.14% in MCC score compared to existing methods.