A blockchain-orchestrated Federated Learning architecture for healthcare consortia

TL;DR

This paper introduces a blockchain-based federated learning architecture tailored for healthcare consortia, integrating privacy-preserving technologies, secure aggregation, and audit trails to enhance data privacy and security.

Contribution

It presents a novel architecture combining blockchain, secure aggregation, and privacy-preserving audit logs specifically designed for healthcare federated learning.

Findings

Designed a new Secure Aggregation protocol with hardware and encryption tools

Implemented privacy-preserving audit trails for healthcare data

Leveraged Ethereum blockchain components for secure federated learning

Abstract

We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs events in the network without revealing identities.