The utilisation of large and diverse datasets for machine learning (ML) at scale is required to promote scientific insight into many meaningful problems. However, due to data governance regulations such as GDPR as well as ethical concerns, the aggregation of personal and sensitive data is problematic, which prompted the development of alternative strategies such as distributed ML (DML). Techniques such as Federated Learning (FL) allow the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind. We further provide recommendations and examples on how such algorithms can be augmented to provide guarantees of governance, security, privacy and verifiability for a general ML audience without prior exposure to formal privacy techniques.

通过分布式机器学习技术，如联邦学习，实现在不泄露个人和敏感数据的情况下进行大规模的机器学习，同时提出隐私保护这一术语无法真正制定隐私定义的风险，并提供建议和示例，帮助非隐私技术专业人员实现算法的治理、安全性、隐私性和可验证性。

分布式机器学习与信任表现