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Federated Learning for the Internet-of-Medical-Things: A Survey

Author

Listed:
  • Vivek Kumar Prasad

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Pronaya Bhattacharya

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Darshil Maru

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Sudeep Tanwar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Ashwin Verma

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Arunendra Singh

    (Department of Information Technology, Pranveer Singh Institute of Technology, Kanpur 209305, Uttar Pradesh, India)

  • Amod Kumar Tiwari

    (Department of Computer Science and Engineering, Rajikiya Engineering College, Sonbhadra 231206, Uttar Pradesh, India)

  • Ravi Sharma

    (Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun 248001, Uttarakhand, India)

  • Ahmed Alkhayyat

    (College of Technical Engineering, The Islamic University, Najaf 54001, Iraq
    Department of Medical instruments Engineering Techniques, Al-Turath University College, Baghdad 10021, Iraq)

  • Florin-Emilian Țurcanu

    (Department of Building Services, Faculty of Civil Engineering and Building Services, Technical University of Gheorghe Asachi, 700050 Iași, Romania)

  • Maria Simona Raboaca

    (National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmicu Vâlcea, 240050 Râmicu Vâlcea, Romania
    Doctoral School, University Politehnica of Bucharest, 060042 Bucharest, Romania
    Faculty of Electrical Engineering and Computer Science, Ștefan cel Mare University, 720229 Suceava, Romania)

Abstract

Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL , is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations.

Suggested Citation

  • Vivek Kumar Prasad & Pronaya Bhattacharya & Darshil Maru & Sudeep Tanwar & Ashwin Verma & Arunendra Singh & Amod Kumar Tiwari & Ravi Sharma & Ahmed Alkhayyat & Florin-Emilian Țurcanu & Maria Simona Ra, 2022. "Federated Learning for the Internet-of-Medical-Things: A Survey," Mathematics, MDPI, vol. 11(1), pages 1-47, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:151-:d:1017881
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    References listed on IDEAS

    as
    1. Vivek Kumar Prasad & Madhuri D. Bhavsar, 2020. "Monitoring IaaS Cloud for Healthcare Systems: Healthcare Information Management and Cloud Resources Utilization," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 11(3), pages 54-70, July.
    2. Vivek Kumar Prasad & Madhuri D. Bhavsar, 2021. "SLAMMP Framework for Cloud Resource Management and Its Impact on Healthcare Computational Techniques," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(2), pages 1-31, March.
    3. David Byrd & Antigoni Polychroniadou, 2020. "Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications," Papers 2010.05867, arXiv.org.
    4. Haokun Fang & Quan Qian, 2021. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning," Future Internet, MDPI, vol. 13(4), pages 1-20, April.
    5. Sushil Kumar Singh & Mikail Mohammed Salim & Jeonghun Cha & Yi Pan & Jong Hyuk Park, 2020. "Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment," Sustainability, MDPI, vol. 12(15), pages 1-23, August.
    6. Tanweer Alam & Ruchi Gupta, 2022. "Federated Learning and Its Role in the Privacy Preservation of IoT Devices," Future Internet, MDPI, vol. 14(9), pages 1-22, August.
    7. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
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    1. Mohammad Heydari & Kin Keung Lai, 2023. "Post-COVID-19 Pandemic Era and Sustainable Healthcare: Organization and Delivery of Health Economics Research (Principles and Clinical Practice)," Mathematics, MDPI, vol. 11(16), pages 1-30, August.

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