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.

本文介绍了一种基于分布式身份技术的隐私保护的分散工作流，用于促进参与者之间可信的联邦学习，限制只有获得适当机构发放的可验证凭证的实体才能建立安全、经过认证的通信渠道，参与与心理健康有关的联邦学习流程。