文献类型：期刊文献

英文题名：FedMIR: Multimodal Federated Learning with Missing Modality Imputation and Distribution-Aware Routing

作者：Xiong, Hongyu[1];Dai, Ming[1,2]

机构：[1]Ocean Univ China, Haide Coll, Qingdao 266100, Peoples R China;[2]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524008, Peoples R China

年份：2026

卷号：26

期号：10

外文期刊名：SENSORS

收录：SCI-EXPANDED(收录号：WOS:001775377300001)、、EI(收录号：20262220807028)、Scopus(收录号：2-s2.0-105040212090)、WOS

基金：This research was supported by a special grant from the Basic and Applied Basic Research Foundation of Guangdong Province [grant number 2023A1515011326] and the Innovation Team Project of General University in Guangdong Province of China [grant number 2024KCXTD042].

语种：英文

外文关键词：multimodal federated learning; internet of things; mixture of experts

外文摘要：Existing multimodal federated learning methods typically assume complete modality availability and struggle with heterogeneity between training and testing data distributions, making them unsuitable for handling missing modalities and distribution drift in distributed learning scenarios such as the Internet of Things (IoT). To address these challenges, we present FedMIR, a novel framework for multimodal federated learning. Our key observation is that heterogeneous modalities can be mapped into a shared semantic space, where cross-modal dependencies can be effectively modeled. Based on this insight, FedMIR leverages contrastive learning to align image-text modalities in a shared latent space and employs conditional generation to reconstruct missing modality representations. The completed representations are then routed through a mixture-of-experts backbone conditioned on the estimated distribution state. FedMIR shares only model parameters and distribution statistics with the server. This design enables the model to operate under missing modality settings while adaptively allocating expert knowledge to cope with distribution drift. We validate FedMIR on federated image-text retrieval benchmarks under heterogeneity and missing data conditions, demonstrating its effectiveness compared to representative federated learning baselines.