  When training a machine learning model, it is standard procedure for the
researcher to have full knowledge of both the data and model. However, this
engenders a lack of trust between data owners and data scientists. Data owners
are justifiably reluctant to relinquish control of private information to third
parties. Privacy-preserving techniques distribute computation in order to
ensure that data remains in the control of the owner while learning takes
place. However, architectures distributed amongst multiple agents introduce an
entirely new set of security and trust complications. These include data
poisoning and model theft. This paper outlines a distributed infrastructure
which is used to facilitate peer-to-peer trust between distributed agents;
collaboratively performing a privacy-preserving workflow. Our outlined
prototype sets industry gatekeepers and governance bodies as credential
issuers. Before participating in the distributed learning workflow, malicious
actors must first negotiate valid credentials. We detail a proof of concept
using Hyperledger Aries, Decentralised Identifiers (DIDs) and Verifiable
Credentials (VCs) to establish a distributed trust architecture during a
privacy-preserving machine learning experiment. Specifically, we utilise secure
and authenticated DID communication channels in order to facilitate a federated
learning workflow related to mental health care data.
