Multimodal Federated Learning in Healthcare: a Review
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arXiv
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| Format: | Preprint |
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2023
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| author | Thrasher, Jacob Devkota, Alina Siwakotai, Prasiddha Chivukula, Rohit Poudel, Pranav Hu, Chaunbo Bhattarai, Binod Gyawali, Prashnna |
| author_facet | Thrasher, Jacob Devkota, Alina Siwakotai, Prasiddha Chivukula, Rohit Poudel, Pranav Hu, Chaunbo Bhattarai, Binod Gyawali, Prashnna |
| contents | Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decentralized mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. The integration of these two concepts supports the ongoing progress of multimodal learning in healthcare while ensuring the security and privacy of patient records within local data-holding agencies. This paper offers a concise overview of the significance of FL in healthcare and outlines the current state-of-the-art approaches to Multimodal Federated Learning (MMFL) within the healthcare domain. It comprehensively examines the existing challenges in the field, shedding light on the limitations of present models. Finally, the paper outlines potential directions for future advancements in the field, aiming to bridge the gap between cutting-edge AI technology and the imperative need for patient data privacy in healthcare applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09650 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multimodal Federated Learning in Healthcare: a Review Thrasher, Jacob Devkota, Alina Siwakotai, Prasiddha Chivukula, Rohit Poudel, Pranav Hu, Chaunbo Bhattarai, Binod Gyawali, Prashnna Machine Learning Artificial Intelligence Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decentralized mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. The integration of these two concepts supports the ongoing progress of multimodal learning in healthcare while ensuring the security and privacy of patient records within local data-holding agencies. This paper offers a concise overview of the significance of FL in healthcare and outlines the current state-of-the-art approaches to Multimodal Federated Learning (MMFL) within the healthcare domain. It comprehensively examines the existing challenges in the field, shedding light on the limitations of present models. Finally, the paper outlines potential directions for future advancements in the field, aiming to bridge the gap between cutting-edge AI technology and the imperative need for patient data privacy in healthcare applications. |
| title | Multimodal Federated Learning in Healthcare: a Review |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.09650 |