Multimodal federated learning (FL) aims to enrich model training in FL settings 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 in heterogeneous network settings: (i) the set of modalities collected by each device will be diverse, and (ii) communication limitations prevent devices from uploading all their locally trained modality models to the server. In this paper, we propose Federated Multimodal Fusion learning with Selective modality communication (FedMFS), a new multimodal fusion FL methodology that can tackle the above mentioned challenges. The key idea is the introduction of a modality selection criterion for each device, which weighs (i) the impact of the modality, gauged by Shapley value analysis, against (ii) the modality model size as a gauge for communication overhead. This enables FedMFS to flexibly balance performance against communication costs, depending on resource constraints and application requirements. Experiments on the real-world ActionSense dataset demonstrate the ability of FedMFS to achieve comparable accuracy to several baselines while reducing the communication overhead by over 4x.

翻译：多模态联邦学习旨在利用设备收集的多模态测量数据（如传感器测量压力、运动及其他类型数据）来丰富联邦学习场景下的模型训练。然而，多模态联邦学习仍面临关键挑战，尤其是在异构网络环境中：其一，各设备收集的模态集合具有多样性；其二，通信限制使得设备无法将所有本地训练的模态模型上传至服务器。本文提出一种新的多模态融合联邦学习方法——基于选择性模态通信的联邦多模态融合学习（FedMFS），以解决上述挑战。其核心思想是为每个设备引入模态选择准则，该准则通过沙普利值分析衡量模态影响力，同时以模态模型大小作为通信开销的衡量指标。这使得FedMFS能够根据资源约束和应用需求，在性能与通信成本之间灵活权衡。在真实世界的ActionSense数据集上的实验表明，FedMFS在实现与多种基线方法相当精度的同时，可将通信开销降低4倍以上。