A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data.

Publication date: Dec 01, 2023

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field’s ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study’s findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97. 11% and an average federated learning accuracy of 93. 95%.

Concepts Keywords
Cyberattacks Blockchain
Diabetes Blockchain
Healthcare Coronavirus
Virus Coronavirus Infections
Education, Medical
Federated Learning
Healthcare IoT
Humans
Internet of Things
Machine Learning
Privacy
Privacy preservation
Smart Contract

Semantics

Type Source Name
disease IDO process
disease VO viable
disease MESH Coronavirus Infections

Original Article

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