Guiding Diffusion Models with Fine-Grained Conditions and Semantics-Preserving Sampling for One-Shot Federated Learning

One-shot Federated Learning (OSFL) has emerged as a promising paradigm to mitigate the high communication overhead of traditional federated learning. However, its effectiveness is often hampered by data heterogeneity across client data. While recent methods leverage pre-trained diffusion models to generate data for OSFL, they often struggle with some practical limitations, including a lack of semantic fidelity in capturing the fine-grained characteristics of local data, and insufficient diversity in the generated data, which collectively degrade the performance of the global model. To address these challenges, we propose \texttt{Espresso}, a novel framework that enhances both the fidelity and diversity of synthetic data in OSFL. \texttt{Espresso} consists of two main components: (1) \textbf{Fine-Grained Condition Learning}, which learns fine-grained conditional embeddings to improve semantic fidelity and diversity by modeling intra-category patterns, and (2) \textbf{Semantics-Preserving Sampling}, which diversifies the generated data by modeling the initial latent noise distribution and applying a self-reflection sampling strategy. Extensive experiments on benchmark datasets demonstrate that \texttt{Espresso} can improve the semantic fidelity and diversity of the synthetic data, leading to a enhancement in the performance of the global model compared to state-of-the-art OSFL methods under data heterogeneity.