<ul><li>The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones.</li><li>As edge intelligence advances, IoT devices are now capable of complex computational tasks, leading to the need for distributed learning strategies to handle multimodal data effectively.</li><li>Multimodal Online Federated Learning (MMO-FL) is introduced as a framework for dynamic and decentralized multimodal learning in IoT environments, addressing challenges like real-time data collection and limited local storage on edge devices.</li><li>A Prototypical Modality Mitigation (PMM) algorithm is proposed within MMO-FL to compensate for missing modalities by leveraging prototype learning, showing superior performance in experimental results.</li></ul>

Multimodal Online Federated Learning with Modality Missing in Internet of Things

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