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Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. (arXiv:2208.03392v3 [cs.LG] UPDATED)
cs.CR updates on arXiv.org arxiv.org
With the advent of the IoT, AI, and ML/DL algorithms, the data-driven medical
application has emerged as a promising tool for designing reliable and scalable
diagnostic and prognostic models from medical data. This has attracted a great
deal of attention from academia to industry in recent years. This has
undoubtedly improved the quality of healthcare delivery. However, these
AI-based medical applications still have poor adoption due to their
difficulties in satisfying strict security, privacy, and quality of service
standards (such …
applications challenges current federated learning future lg medical research trends