Gradient Boosting for Health IoT Federated Learning

Sobia Wassan (), Beenish Suhail, Riaqa Mubeen, Bhavana Raj, Ujjwal Agarwal, Eti Khatri, Sujith Gopinathan and Gaurav Dhiman
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Sobia Wassan: School of Equipment Engineering, Jiangsu Urban and Rural Construction Vocational College, Changzhou 213000, China
Beenish Suhail: School of Economics, Shanghai University, Shanghai 201900, China
Riaqa Mubeen: School of Management, Harbin Institute of Technology (HIT), Harbin 150001, China
Bhavana Raj: School of Management, Institute of Public Enterprise, Hyderabad 500101, India
Ujjwal Agarwal: School of Information Technology, University of Technology and Applied Sciences, Salalah 215, Oman
Eti Khatri: School of Management, Nitte Meenakshi Institute of Technology, Bengaluru 560064, India
Sujith Gopinathan: School of Finance, AMU/AIMA, New Delhi 110003, India
Gaurav Dhiman: Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon

Sustainability, 2022, vol. 14, issue 24, 1-17

Abstract: Federated learning preserves the privacy of user data through Machine Learning (ML). It enables the training of an ML model during this process. The Healthcare Internet of Things (HIoT) can be used for intelligent technology, remote detection, remote medical care, and remote monitoring. The databases of many medical institutes include a vast quantity of medical information. Nonetheless, based on its specific nature of health information, susceptibilities to private information, and since it cannot be pooled related to data islands, Federated Learning (FL) offers a solution as a shared collaborative artificial intelligence technology. However, FL addresses a series of security and privacy issues. An adaptive Differential Security Federated Learning Healthcare IoT (DPFL-HIoT) model is proposed in this study. We propose differential privacy federated learning with an adaptive GBTM model algorithm for local updates, which helps adapt the model’s parameters based on the data characteristics and gradients. By training and applying a Gradient Boosted Trees model, the GBTM model identifies medical fraud based on patient information. This model is validated to check performance. Real-world experiments show that our proposed algorithm effectively protects data privacy.

Keywords: data privacy; federated learning; medical fraud; Internet of Things; GBTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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