Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data

Abstract

Machine learning and blockchain technology have been explored for potential applications in medicine with only modest success to date. Focus has shifted to exploring the intersection of these technologies along with other privacy preserving encryption techniques for better utility. This combination applied to federated learning, which allows remote execution of function and analysis without the need to move highly regulated personal health information, seems to be the key to successful applications of these technologies to rapidly advance evidence-based medicine. We give a brief history of these technologies in medicine, outlining some of the challenges with successful use. We then explore a more detailed combination of usage with an emphasis on decentralizing or federating the learning process along with auditability and incentivization blockchain can allow in the machine learning process. Based on the cost-benefit analysis of previous efforts, we provide the framework for an advanced blockchain-orchestrated machine learning system for privacy preserving federated learning in medicine and a new utility in health. Six critical elements for this approach in the future will be:(a) Data and analytic processes discoverable on secure public blockchain while retaining privacy of the data and analytic processes(b) Value fabricated by generating data/compute matches that were previously illegal, unethical and infeasible(c) Compute guarantees provided by federated learning and advanced cryptography(d) Privacy guarantees provided by software (e.g., Homomorphic Encryption, Secure Multi-Party Computation, …) and hardware (e.g., Intel SGX and AMD SEV-SNP) cryptography(e) Data quality incentivized via tokenized reputation-based rewards(f) Discarding of poor data accomplished via model poisoning attack prevention techniques