تقرير
Multimodal Federated Learning in Healthcare: a Review
| العنوان: | Multimodal Federated Learning in Healthcare: a Review |
|---|---|
| المؤلفون: | Thrasher, Jacob, Devkota, Alina, Siwakotai, Prasiddha, Chivukula, Rohit, Poudel, Pranav, Hu, Chaunbo, Bhattarai, Binod, Gyawali, Prashnna |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: 28 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.09650 |
| رقم الأكسشن: | edsarx.2310.09650 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |