Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

TL;DR

This paper proposes a decentralized machine learning approach for health care systems using distributed ledgers, enabling privacy-preserving, efficient, and scalable analysis of electronic health records across multiple institutions.

Contribution

It introduces a novel decentralized EHR framework leveraging distributed ledgers for improved privacy, reduced latency, and better utilization of personal medical device data.

Findings

Up to 60% reduction in machine learning time.

Consensus latency below 8 seconds.

Supports anonymous predictive analysis across institutions.

Abstract

The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.