BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities
Multimodal federated learning (FL) is essential for real-world applications such as autonomous systems and healthcare, where data is distributed across heterogeneous clients with varying and often missing modalities. However, most existing FL approaches assume uniform modality availability, limiting their applicability in practice. We introduce BLOSSOM, a task-agnostic framework for multimodal FL designed to operate under shared and sparsely observed modality conditions. BLOSSOM supports clients with arbitrary modality subsets and enables flexible sharing of model components. To address client and task heterogeneity, we propose a block-wise aggregation strategy that selectively aggregates...
| Authors | Pranav M R, Jayant Chandwani, Ahmed M. Abdelmoniem et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 14 April 2026 |
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