Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

@article{Rauniyar2022FederatedLF,
  title={Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions},
  author={Ashish Rauniyar and Desta Haileselassie Hagos and Debesh Jha and Jan Erik Haakegaard and Ulas Bagci and Danda B. Rawat and Vladimir Vlassov},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.03392}
}
—With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (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… 

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