论文标题
通过联合学习的数字健康的未来
The Future of Digital Health with Federated Learning
论文作者
论文摘要
数据驱动的机器学习已成为一种有前途的方法，用于从医学数据中构建准确，稳健的统计模型，这是现代医疗保健系统在庞大的批量中收集的。现有的医疗数据并未完全由ML充分利用，主要是因为它位于数据孤岛中，而隐私涉及限制对该数据的访问。但是，如果没有足够的数据访问，将阻止ML发挥其全部潜力，并最终从研究到临床实践过渡。本文考虑了导致这个问题的关键因素，探讨了联邦学习（FL）如何为数字健康的未来提供解决方案，并突出了需要解决的挑战和考虑因素。
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
