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Multimodal federated learning (FL) aims to enrich model training in FLsettings where devices are collecting measurements across multiple modalities(e.g., sensors measuring pressure, motion, and other types of data). However,key challenges to multimodal FL remain unaddressed, particularly inheterogeneous network settings: (i) the set of modalities collected by eachdevice will be diverse, and (ii) communication limitations prevent devices fromuploading all their locally trained modality models to the server. In thispaper, we propose Federated Multimodal Fusion learning with Selective modalitycommunication (FedMFS), a new multimodal fusion FL methodology that can tacklethe above mentioned challenges. The key idea is the introduction of a modalityselection criterion for each device, which weighs (i) the impact of themodality, gauged by Shapley value analysis, against (ii) the modality modelsize as a gauge for communication overhead. This enables FedMFS to flexiblybalance performance against communication costs, depending on resourceconstraints and application requirements. Experiments on the real-worldActionSense dataset demonstrate the ability of FedMFS to achieve comparableaccuracy to several baselines while reducing the communication overhead by over4x.

FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication