[2603.19337] Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19337 (cs) [Submitted on 19 Mar 2026] Title:Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity Authors:Jing Liu, Zhengliang Guo, Yan Wang, Xiaoguang Zhu, Yao Du, Zehua Wang, Victor C. M. Leung View a PDF of the paper titled Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity, by Jing Liu and 6 other authors View PDF HTML (experimental) Abstract:Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in multimodal perception settings. Conventional methods often fail to address the underlying semantic discrepancies between clients, leading to suboptimal performance for multimedia systems requiring robust perception. To overcome this, we introduce SemanticFL, a novel framework that leverages the rich semantic representations of pre-trained diffusion models to provide privacy-preserving guidance for local training. Our approach leverages multi-layer semantic representations from a pre-trained Stable Diffusion model (including VAE-encoded latents and U-Net hierarchical features) to create a shared latent space that aligns heterogeneous clients, facilitated by an efficient client-server architecture that offloads heavy computation to the server. A unified consistency mechanism, employing cross-modal contrastive learning, further stabilizes convergence. We cond...