A blockchain-orchestrated Federated Learning architecture for healthcare consortia
This addresses privacy and security challenges for healthcare consortia, but it appears incremental as it builds on existing blockchain and federated learning methods.
The paper tackles the problem of federated learning in healthcare consortia by proposing a novel architecture that integrates blockchain and privacy-preserving technologies, resulting in a new Secure Aggregation protocol and a privacy-preserving audit trail.
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.