Currently, many contexts exist where distributed learning is difficult or otherwise constrained by security and communication limitations. One common domain where this is a consideration is in Healthcare where data is often governed by data-use-ordinances like HIPAA. On the other hand, larger sample sizes and shared data models are necessary to allow models to better generalize on account of the potential for more variability and balancing underrepresented classes. Federated learning is a type of distributed learning model that allows data to be trained in a decentralized manner. This, in turn, addresses data security, privacy, and vulnerability considerations as data itself is not shared across a given learning network nodes. Three main challenges to federated learning include node data is not independent and identically distributed (iid), clients requiring high levels of communication overhead between peers, and there is the heterogeneity of different clients within a network with respect to dataset bias and size. As the field has grown, the notion of fairness in federated learning has also been introduced through novel implementations. Fairness approaches differ from the standard form of federated learning and also have distinct challenges and considerations for the healthcare domain. This paper endeavors to outline the typical lifecycle of fair federated learning in research as well as provide an updated taxonomy to account for the current state of fairness in implementations. Lastly, this paper provides added insight into the implications and challenges of implementing and supporting fairness in federated learning in the healthcare domain.

翻译：目前，在许多场景下，分布式学习因安全和通信限制而难以实现或受到约束。一个常见的应用领域是医疗健康，其中数据通常受到类似HIPAA（健康保险携带与责任法案）等数据使用法规的约束。另一方面，更大的样本量和共享数据模型对于提升模型的泛化能力至关重要，这有助于应对数据多样性以及平衡代表性不足的类别。联邦学习是一种分布式学习模型，允许以去中心化的方式训练数据。这解决了数据安全、隐私和脆弱性问题，因为数据本身不会在学习网络节点之间共享。联邦学习面临的三个主要挑战包括：节点数据非独立同分布、客户端之间需要高通信开销，以及网络中各客户端在数据集偏差和规模上的异质性。随着该领域的发展，通过新颖的实现方式，联邦学习中的公平性概念也被引入。公平性方法不同于标准的联邦学习形式，在医疗健康领域具有独特的挑战和考量。本文旨在概述研究中公平联邦学习的典型生命周期，并提供一个更新的分类法，以反映公平性实现的当前状态。最后，本文深入探讨了在医疗健康领域实施和支持联邦学习公平性的影响与挑战。