Privacy and Trust Redefined in Federated Machine Learning

TL;DR

This paper proposes a decentralized, privacy-preserving federated learning framework utilizing blockchain-based identity verification to enable secure collaboration on sensitive mental health data.

Contribution

It introduces a novel trust framework using decentralized identity technologies for secure federated learning with sensitive data.

Findings

Successful implementation of a decentralized trust framework

Secure, authenticated communication channels established

Enhanced privacy preservation in federated learning environments

Abstract

A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.