Comparative Analysis of GAN-Based Synthetic Data and Traditional Augmentation for Non-IID Federated Learning on GLUE Tasks

Abstract

Abstract: Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this paper, we propose a novel framework, named Synthetic Data Aided Federated Learning (SDA-FL), to resolve this non-IID challenge by sharing synthetic data. Specifically, each client pretrains a local generative adversarial network (GAN) to generate differentially private synthetic data, which are uploaded to the parameter server (PS) to construct a global shared synthetic datase

Research Question

How does the integration of GAN-based synthetic data generation in SDA-FL compare to traditional data augmentation techniques (e.g., SMOTE, MixUp) in terms of improving federated learning accuracy on GLUE tasks under non-IID distributions?

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