In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare - Topaloglu, Mustafa Y.; Morrell, Elisabeth M.; Rajendran, Suraj; Topaloglu, Umit (Frontiers in Artificial Intelligence, 2021)
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 individual contributions to a FL implementation, which will be essential for sharing any profit and responsibilities that may accompany the FL project.