Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine^1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes³. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation^4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

快速可靠地检测患有严重和异质性疾病的患者是精准医学^1,2的主要目标。^{可以根据血液转录组3}使用机器学习来识别白血病患者。然而，由于隐私立法^{4,5 ，技术上可行的和允许的之间的差距越来越大}. 在这里，为了在不违反隐私法的情况下促进来自全球任何数据所有者的任何医疗数据的集成，我们引入了 Swarm Learning——一种分散的机器学习方法，它结合了边缘计算、基于区块链的点对点网络和协调，同时保持机密性无需中央协调员，从而超越了联邦学习。为了说明使用 Swarm Learning 使用分布式数据开发疾病分类器的可行性，我们选择了四种异质性疾病用例（COVID-19、结核病、白血病和肺病）。拥有来自 127 项临床研究的 16,400 多个血液转录组，这些临床研究的病例和对照分布不均匀，存在大量研究偏差，以及 95,000 多张胸部 X 光图像，我们表明 Swarm 学习分类器优于在单个站点开发的分类器。此外，Swarm Learning 在设计上完全符合当地的保密规定。我们相信，这种方法将显着加速精准医学的引入。