A blockchain-orchestrated Federated Learning architecture for healthcare
consortia
consortia
File(s)
Working paper
Author(s)
Type
Working Paper
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.
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.
Date Issued
2019-10-12
Citation
2019
Publisher
arXiv
Copyright Statement
© 2019 The Author(s)
Identifier
http://arxiv.org/abs/1910.12603v1
Subjects
cs.CY
cs.CY
cs.CR
cs.LG
Publication Status
Published