In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

Frontiers in Artificial Intelligence

Suraj Rajendran¹  Umit Topaloglu¹  Elisabeth M. Morrell²  Mustafa Y. Topaloglu² 

[1] Wake Forest School of Medicine, Winston Salem, NC, United States;Wake Forest University, Winston Salem, NC, United States;

关键词: machine learning;    federated learning;    data sharing;    machine learning legal and ethical issues;    machine learning social and regulatory issues;   

DOI  :  10.3389/frai.2021.746497

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown