In recent years, privacy and security concerns in machine learning have
promoted trusted federated learning to the forefront of research. Differential
privacy has emerged as the de facto standard for privacy protection in
federated learning due to its rigorous mathematical foundation and provable
guarantee. Despite extensive research on algorithms that incorporate
differential privacy within federated learning, there remains an evident
deficiency in systematic reviews that categorize and synthesize these studies.
Our work presents a systematic overview of the differentially private
federated learning. Existing taxonomies have not adequately considered objects
and level of privacy protection provided by differential privacy in federated
learning. To rectify this gap, we propose a new taxonomy of differentially
private federated learning based on definition and guarantee of differential
privacy and federated scenarios. Our classification allows for a clear
delineation of the protected objects across various differential privacy models
and their respective neighborhood levels within federated learning
environments. Furthermore, we explore the applications of differential privacy
in federated learning scenarios. Our findings provide valuable insights into
privacy-preserving federated learning and suggest practical directions for
future research.

我们的研究针对差分隐私的联邦学习进行了系统的概述和分类，提出了一种新的基于差分隐私和联邦场景定义和保证的分类方法，并探讨了差分隐私在联邦学习场景中的应用，为隐私保护的联邦学习提供了有价值的洞见和未来研究方向。

差分隐私联邦学习：系统性综述

Differentially Private Federated Learning: A Systematic Review

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。