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

Vivek Kumar Prasad, Pronaya Bhattacharya, Darshil Maru, Sudeep Tanwar (), Ashwin Verma, Arunendra Singh, Amod Kumar Tiwari, Ravi Sharma, Ahmed Alkhayyat, Florin-Emilian Țurcanu () and Maria Simona Raboaca
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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
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

Mathematics, 2022, vol. 11, issue 1, 1-47

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.

Keywords: federated Learning; healthcare; cloud computing; security; privacy; blockchain; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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