Federated learning (FL) demonstrates its advantages in integrating
distributed infrastructure, communication, computing and learning in a
privacy-preserving manner. However, the robustness and capabilities of existing
FL methods are challenged by limited and dynamic data and conditions,
complexities including heterogeneities and uncertainties, and analytical
explainability. Bayesian federated learning (BFL) has emerged as a promising
approach to address these issues. This survey presents a critical overview of
BFL, including its basic concepts, its relations to Bayesian learning in the
context of FL, and a taxonomy of BFL from both Bayesian and federated
perspectives. We categorize and discuss client- and server-side and FL-based
BFL methods and their pros and cons. The limitations of the existing BFL
methods and the future directions of BFL research further address the intricate
requirements of real-life FL applications.

本文是一项关于贝叶斯联邦学习（BFL）的问题与应用的调查研究，旨在解决分布式基础设施、通信、计算和隐私保护等问题，讨论客户端、服务端和基于 FL 的 BFL 方法，并提出了 BFL 研究的未来发展方向。

贝叶斯联邦学习：综述

Bayesian Federated Learning: A Survey

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.

本文介绍了一种分布式基础设施，用于实现分布式代理之间的点对点信任，从而协同执行保护隐私的机器学习工作流程。使用 Hyperledger Aries，Decentralised Identifiers (DIDs) 和 Verifiable Credentials (VCs) 建立了一个分布式信任体系，促进与心理健康护理相关的联邦学习工作流程。