In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare.
In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare.
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DOI：
10.3389/frai.2021.746497
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发表时间：
2021
影响因子：
4
通讯作者：
Topaloglu U
中科院分区：
文献类型：
--
作者：
Topaloglu MY;Morrell EM;Rajendran S;Topaloglu U
人工智能及其子领域机器学习（ML）已经显示出在医疗保健领域产生前所未有的影响的潜力。引入联合学习（FL）是为了减轻ML的一些限制，特别是在更大的数据集上进行训练以提高性能的能力，由于现有的患者保护法律和法规，这对于机构间协作来说通常很麻烦。此外，FL还可以通过访问跨地理分布位置的代表性不足的群体的数据，在规避ML的迫切偏见问题方面发挥至关重要的作用。在本文中，我们讨论了外语的三个挑战，即：隐私的模式交流，伦理的角度来看，和法律的考虑。最后，我们提出了一个模型，可以帮助评估FL实现的数据贡献。鉴于在更有限的情况下使用Sørensen-Dice系数的便利性和适应性（例如，水平FL）和计算昂贵的Shapley值，我们试图证明一个新的范例，我们希望，将成为分享任何利润和责任，可能伴随着FL的奋进无价。
Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML’s exigent bias problem by accessing underrepresented groups’ data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen–Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.
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