FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks

With the advancement of data collection techniques, end users are interested in how different types of data can collaborate to improve our life experiences. Multimodal Federated Learning (MFL) is an emerging area allowing many distributed clients, each of which can collect data from multiple types of sensors, to participate in the training of some multimodal data-related models without sharing their data. In this paper, we address a novel challenging issue in MFL, the modality incongruity, where clients may have heterogeneous setups of sensors and their local data consists of different combinations of modalities. With the modality incongruity, clients may solve different tasks on different parameter spaces, which escalates the difficulties in dealing with the statistical heterogeneity problem of federated learning; also, it would be hard to perform accurate model aggregation across different types of clients. To tackle these challenges, in this work, we propose the FedMSplit framework, which allows federated training over multimodal distributed data without assuming similar active sensors in all clients. The key idea is to employ a dynamic and multi-view graph structure to adaptively capture the correlations amongst multimodal client models. More specifically, we split client models into smaller shareable blocks and allow each type of blocks to provide a specific view on client relationships. With the graph representation, the underlying correlations between clients can be captured as the edge features in the multi-view graph, and then be utilized to promote local model relations through the neighborhood message passing in the graph. Our experimental results demonstrate the effectiveness of our method under different sensor setups with statistical heterogeneity.