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

Distributed Machine Learning and the Semblance of Trust

Thanks to the recent advances in processing speed and data acquisition and
storage, machine learning (ML) is penetrating every facet of our lives, and
transforming research in many areas in a fundamental manner. Wireless
communications is another success story -- ubiquitous in our lives, from
handheld devices to wearables, smart homes, and automobiles. While recent years
have seen a flurry of research activity in exploiting ML tools for various
wireless communication problems, the impact of these techniques in practical
communication systems and standards is yet to be seen. In this paper, we review
some of the major promises and challenges of ML in wireless communication
systems, focusing mainly on the physical layer. We present some of the most
striking recent accomplishments that ML techniques have achieved with respect
to classical approaches, and point to promising research directions where ML is
likely to make the biggest impact in the near future. We also highlight the
complementary problem of designing physical layer techniques to enable
distributed ML at the wireless network edge, which further emphasizes the need
to understand and connect ML with fundamental concepts in wireless
communications.

本文回顾了机器学习在物理层无线通信系统中的应用及其带来的一些挑战，并着重介绍了机器学习在无线通信领域中的一些前沿技术及其在未来的研究方向，同时强调了基于物理层设计的分布式机器学习的可行性和需求。