Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity

ArXi:2603.19337v1 Announce Type: cross 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