Accurate and fair medical machine learning requires large amounts and diverse data to train on. Privacy-preserving methods such as federated learning can help improve machine learning models by making use of datasets in different hospitals and institutes while the data stays where it is collected.

中文翻译：

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无需共享数据的协作学习

准确和公平的医疗机器学习需要大量多样的数据进行训练。联邦学习等隐私保护方法可以通过使用不同医院和研究所的数据集来帮助改进机器学习模型，同时数据保留在收集处。