In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources further enhances its potential. Multimodal Federated Learning (MFL) is a distributed approach that enhances the efficiency and quality of multimodal learning, ensuring collaborative work and privacy protection. However, missing modalities pose a significant challenge in MFL, often due to data quality issues or privacy policies across the clients. In this work, we present MMiC, a framework for Mitigating Modality incompleteness in MFL within the Clusters. MMiC replaces partial parameters within client models inside clusters to mitigate the impact of missing modalities. Furthermore, it leverages the Banzhaf Power Index to optimize client selection under these conditions. Finally, MMiC employs an innovative approach to dynamically control global aggregation by utilizing Markovitz Portfolio Optimization. Extensive experiments demonstrate that MMiC consistently outperforms existing federated learning architectures in both global and personalized performance on multimodal datasets with missing modalities, confirming the effectiveness of our proposed solution. Our code is available at https://github.com/gotobcn8/MMiC.

The significant increase in data generation across various sectors has prompted the development of concepts such as Data Product and Data Economy (DE) to enhance organizational productivity. Concurrently, advancements in AI models have heightened data privacy concerns, particularly as typical AI model training methods often involve data collection and storage in centralized databases, which are exposed to misuse. In response, Federated Learning (FL) has emerged as a promising approach, enabling the collaborative training of AI models without the direct sharing of data. This paper examines the potential of FL in the initial stages of data generation and throughout the data product design process. It further explores how FL can facilitate the generation of data products, providing a range of practical applications across different industries to address privacy concerns effectively in modern AI solutions.