ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
arXiv:2607.06633v1 Announce Type: cross 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 Mix
The increasing complexity of real-world data and the need for privacy-preserving machine learning solutions across diverse datasets necessitate advancements in federated learning with multimodal capabilities.
This development allows for more robust and private AI solutions, especially in fields like healthcare that deal with sensitive and often incomplete multimodal patient data, accelerating the adoption of distributed AI.
Multimodal federated learning can now better handle missing data without relying on public datasets or simplistic feature synthesis, enhancing data utility and privacy simultaneously.
- · Healthcare sector
- · Federated learning platforms
- · AI model developers
- · Cloud providers with distributed AI offerings
- · Centralized data processing models
- · AI systems requiring complete datasets
Improved accuracy and applicability of AI models trained on disparate, incomplete multimodal datasets.
Increased adoption of federated learning in sectors with strict privacy and data sovereignty requirements, leading to new vertical-specific AI innovations.
The development of global, privacy-preserving AI insights that leverage vast but fragmented data, potentially leading to new forms of collaborative intelligence.
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Read at arXiv cs.AI