Xiaojun Deng

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 hindered by data heterogeneity across clients. While recent methods leverage pre-trained diffusion models to gener