ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
This paper proposes ProMoE-FL, a prototype-conditioned mixture-of-experts framework for robust missing-modality feature synthesis in multimodal federated learning. It builds a global client-aware prototype bank capturing clinically meaningful modality priors across institutions, and uses direction-aware expert routing to dynamically synthesize missing features. Experiments on four chest X-ray datasets show consistent outperformance over state-of-the-art methods in both homogeneous and heterogeneous settings.
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[Submitted on 7 Jul 2026]
Title:ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
View a PDF of the paper titled ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities, by Aavash Chhetri and Bibek Niroula and Eduard Vazquez and Yash Raj Shrestha and Prashnna Gyawali and Loris Bazzani and Binod Bhattarai
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Abstract:In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or perform naive feature synthesis that is based solely on the available modality. To address these limitations, we propose ProMoE-FL, a Prototype-conditioned Mixture-of-Experts framework for robust missing-modality feature synthesis in multimodal federated learning. ProMoE-FL builds a global client-aware prototype bank that captures clinically meaningful modality priors across institutions. Our Mixture of Experts is conditioned on these prototypes and modality indices to enable direction-aware expert routing for dynamically synthesizing missing features. We perform extensive quantitative and qualitative evaluations on four public chest X-ray datasets (MIMIC-CXR, NIH Open-I, PadChest, and CheXpert) and demonstrate that ProMoE-FL consistently outperforms state-of-the-art methods in both homogeneous as well as the more challenging heterogeneous settings.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.06633 [cs.CV]
(or arXiv:2607.06633v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.06633
arXiv-issued DOI via DataCite
Submission history
From: Bibek Niroula [view email] [v1] Tue, 7 Jul 2026 13:43:20 UTC (2,256 KB)
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