Many ethical and legal challenges surround COVID-19 data analysis, including data ownership, data security, and privacy issues. As a result, healthcare providers have typically preferred models validated on their own data. However, this limits the scope of analysis that can be performed, often resulting in AI models that lack diversity, suffer from overfitting, and demonstrate poor generalization. One recent study titled Federated learning for predicting clinical outcomes in patients with COVID-19, published in September 15 issue of Nature Medicine [1], offered a solution to these problems: Federated Learning (FL). FL is a privacy-protection model trained in heterogeneous, distributed networks [2].