← 研踪 PaperTrailCVF · CVPR 2026:cvf:f1eade63ba9e8d1f7c8ebcdb91600a28 · 记录 #1
Guiding Diffusion Models with Fine-Grained Conditions and Semantics-Preserving Sampling for One-Shot Federated Learning
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:06
摘要
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 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 Espresso, a novel framework that enhances both the fidelity and diversity of synthetic data in OSFL. Espresso consists of two main components: (1) Fine-Grained Condition Learning, which learns fine-grained conditional embeddings to improve semantic fidelity and diversity by modeling intra-category patterns, and (2) Semantics-Preserving Sampling, which diversifies the generated data by modeling the distribution of latent noises and applying a self-reflection sampling strategy. Extensive experiments on benchmark datasets demonstrate that Espresso can improve the semantic fidelity and diversity of the synthetic data, leading to an enhancement in the performance of the global model compared to state-of-the-art OSFL methods under data heterogeneity.
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记录 #1 · 2026 · 最新观测版本
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 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 Espresso, a novel framework that enhances both the fidelity and diversity of synthetic data in OSFL. Espresso consists of two main components: (1) Fine-Grained Condition Learning, which learns fine-grained conditional embeddings to improve semantic fidelity and diversity by modeling intra-category patterns, and (2) Semantics-Preserving Sampling, which diversifies the generated data by modeling the distribution of latent noises and applying a self-reflection sampling strategy. Extensive experiments on benchmark datasets demonstrate that Espresso can improve the semantic fidelity and diversity of the synthetic data, leading to an enhancement in the performance of the global model compared to state-of-the-art OSFL methods under data heterogeneity.
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