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arXiv · 2611.03032

Hybrid Federated Learning for Multimodal Fusion under Missing Modalities and Site Heterogeneity

Xiaofei Wu·Rongmei Liang·Jian Qing Shi
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Abstract

Multimodal fusion is studied under hybrid federation, where data are partitioned across samples and feature blocks. The proposed two-dimensional federated smooth multiple-kernel large-margin learning method (2D-FedSMKL) integrates availability-corrected modality scores, group-kernel regularization, and smooth worst-site risk aggregation. For fixed maps and propensities, the objective is convex and smooth. Curvature bounds and exact synchronous block-gradient equivalence are established for binary and multiclass scores, giving a conditional accelerated rate. An oracle-propensity binary risk bound and a propensity-perturbation result are also derived. Simulations and two public activity-recognition benchmarks assess site heterogeneity and modality absence. Worst-site aggregation improves worst-site performance under heterogeneity, including reversed and size-balanced designs. On the benchmarks, validation selects the correction; the selected model is not significantly worse than the strongest reference at the worst site, and group-regularization benefits vary by dataset.

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Xiaofei Wu, Rongmei Liang, Jian Qing Shi. 2026-11-02. Hybrid Federated Learning for Multimodal Fusion under Missing Modalities and Site Heterogeneity. https://arxiv.org/abs/2611.03032

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