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Privacy-Preserving Digital Intervention for Mental Health Using Federated Learning

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Intelligent Human Computer Interaction (IHCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13741))

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Abstract

Digital health care is emerging along with the growth and acceptability of modern ICT technologies such as IoT and Artificial Intelligence. Today’s many health services are being served by computer-related technologies like telemedicine, teleconsultation, and remote diagnosis. However, mental health still lacks ICT technology integration due to privacy and highly sensitive data sharing. Recently, privacy-preserving technologies have been researched and applied to various privacy-required domains. To harness the benefits of these technologies and address the bottleneck of existing solutions in mental health-related solutions, we have proposed a federated learning-based solution for classifying human emotion into seven classes adoration, amusement, anxiety, disgust, empathic pain, and fear and surprise. Our proposed solution preserves data privacy while providing classification accuracy nearly equal to the traditional centralized machine learning solutions.

This research was supported by the Ministry of Science and ICT (MSIT) Korea under the National Research Foundation (NRF) Korea (NRF-2022R1A2C4001270), by the MSIT Korea Korea under the India-Korea Joint Programme of Cooperation in Science & Technology (NRF-2020K1A3A1A68093469), and by the ITRC (Information Technology Research Center) support program (IITP-2020-2020-0-01602) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

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Notes

  1. 1.

    https://www.reuters.com/markets/deals/amazon-buy-one-medical-35-billion-deal-2022-07-21/.

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Acknowledgement

This research was supported by the MSIT Korea under the NRF Korea (NRF-2022R1A2C4001270), by the MSIT Korea Korea under the India-Korea Joint Programme of Cooperation in Science & Technology (NRF-2020K1A3A1A68093469), and by the ITRC support program (IITP-2020-2020-0-01602) supervised by the IITP.

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Authors and Affiliations

  1. Soongsil University, Seoul, South Korea

    Ankit Kumar Singh, Ajit Kumar & Bong Jun Choi

Authors
  1. Ankit Kumar Singh
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  2. Ajit Kumar
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  3. Bong Jun Choi
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Corresponding author

Correspondence to Ajit Kumar .

Editor information

Editors and Affiliations

  1. Tashkent University Information Technologies, Tashkent, Uzbekistan

    Hakimjon Zaynidinov

  2. Oregon Institute of Technology, Klamath Falls, USA

    Madhusudan Singh

  3. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  4. Hankuk University of Foreign Studies, Yongin, Korea (Republic of)

    Dhananjay Singh

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Singh, A.K., Kumar, A., Choi, B.J. (2023). Privacy-Preserving Digital Intervention for Mental Health Using Federated Learning. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_22

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  • DOI: https://doi.org/10.1007/978-3-031-27199-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27198-4

  • Online ISBN: 978-3-031-27199-1

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