Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

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医学背景下的联邦学习：系统文献综述

数据隐私是一个非常重要的问题。尤其是在医学等领域，遵守现有的隐私法规以保护患者的匿名性至关重要。然而，研究和训练机器学习模型需要数据，这些数据可以帮助深入了解复杂的相关性或个性化治疗，否则这些数据可能未被发现。这些模型通常会随着可用数据量的增加而扩展，但目前的情况通常禁止跨站点构建大型数据库。因此，能够组合来自世界各地不同站点的相似或相关数据，同时仍然保护数据隐私将是有益的。联邦学习已被提出作为解决方案，因为它依赖于机器学习模型的共享，而不是原始数据本身。这意味着私人数据永远不会离开收集它的网站或设备。联邦学习是一个新兴的研究领域，已经确定了许多应用这些方法的领域。这篇系统的文献综述对联邦学习的概念和研究及其对机密医疗保健数据集的适用性进行了广泛的研究。