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Federated Learning: Issues in Medical Application

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Future Data and Security Engineering (FDSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13076))

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Abstract

Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.

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Acknowledgement

This research was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00990, Platform Development and Proof of High Trust & Low Latency Processing for Heterogeneous\(\cdot \)Atypical\(\cdot \)Large Scaled Data in 5G-IoT Environment).

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Authors and Affiliations

  1. College of Computing and Informatics, Sungkyunkwan University, Suwon, Republic of Korea

    Joo Hun Yoo, Hyejun Jeong, Jaehyeok Lee & Tai-Myoung Chung

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  1. Joo Hun Yoo
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  2. Hyejun Jeong
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  3. Jaehyeok Lee
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  4. Tai-Myoung Chung
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Contributions

J.H.Y. and H.J. are co-first authors and contributed equally to the composition and preparation of the paper. T-M.C. proposed research and theorem in the field of federated learning in medical application. J.H.Y., H.J., J.L., and T-M.C. discussed about suggested research idea. J.H.Y. and H.J. wrote the manuscript with support from J.L.. H.J. revised the entire manuscript. T-M.C. provided invaluable guidance throughout the research and the writing of the manuscript. All authors have reviewed the manuscript.

Corresponding author

Correspondence to Tai-Myoung Chung .

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Editors and Affiliations

  1. HCMC University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Koganei-shi, Tokyo, Japan

    Makoto Takizawa

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Yoo, J.H., Jeong, H., Lee, J., Chung, TM. (2021). Federated Learning: Issues in Medical Application. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_1

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  • DOI: https://doi.org/10.1007/978-3-030-91387-8_1

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