Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities

Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin, Sung-Ju Lee

Keywords: federated learning, incomplete modalities, time-series healthcare sensing

Abstract:

Many healthcare sensing applications utilize multimodal time-series data from sensors embedded in mobile and wearable devices. Federated Learning (FL), with its privacy-preserving advantages, is particularly well-suited to such applications. However, most multimodal FL methods assume the availability of complete modality data for local training, which is often unrealistic. Moreover, recent approaches for tackling incomplete modalities scale poorly and become inefficient as the number of modalities increases. To address these limitations, we propose FLISM, an innovative algorithm that enables efficient FL training with incomplete sensing modalities while maintaining high accuracy. FLISM employs three key techniques: (1) modality-invariant representation learning to extract effective features from clients with a diverse set of modalities, (2) modality quality-aware aggregation to prioritize contributions from clients with higher-quality data, and (3) global-aligned knowledge distillation to mitigate local update shifts caused by modality differences. Extensive experiments on real-world datasets show that FLISM not only achieves high accuracy but is also faster and more efficient compared to state-of-the-art methods designed to handle incomplete modality problems in FL.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 9015