  The proliferation of edge devices has brought Federated Learning (FL) to the
forefront as a promising paradigm for decentralized and collaborative model
training while preserving the privacy of clients' data. However, FL struggles
with a significant performance reduction and poor convergence when confronted
with Non-Independent and Identically Distributed (Non-IID) data distributions
among participating clients. While previous efforts, such as client drift
mitigation and advanced server-side model fusion techniques, have shown some
success in addressing this challenge, they often overlook the root cause of the
performance reduction - the absence of identical data accurately mirroring the
global data distribution among clients. In this paper, we introduce Gen-FedSD,
a novel approach that harnesses the powerful capability of state-of-the-art
text-to-image foundation models to bridge the significant Non-IID performance
gaps in FL. In Gen-FedSD, each client constructs textual prompts for each class
label and leverages an off-the-shelf state-of-the-art pre-trained Stable
Diffusion model to synthesize high-quality data samples. The generated
synthetic data is tailored to each client's unique local data gaps and
distribution disparities, effectively making the final augmented local data
IID. Through extensive experimentation, we demonstrate that Gen-FedSD achieves
state-of-the-art performance and significant communication cost savings across
various datasets and Non-IID settings.
