During a pandemic, such as COVID-19, the scientific community must optimize collaboration, as part of the race against time to identify and repurpose existing treatments. Today, Artificial Intelligence (AI) offers us a significant opportunity to generate insights and provide predictive models that could substantially improve the opportunities for understanding the core metrics that characterize the epidemic. A principal barrier for effective AI models in a collaborative environment, especially in the medical and pharmaceutical industries, is dealing with datasets that are distributed across multiple organizations, as traditional AI models rely on the datasets being in one location. In the status quo, organizations must slog through a costly and time-consuming process of extract-transform-loading to build a dataset in a singular location. This paper addresses how Federated Learning may be applied to facilitate flexible AI models that have been trained on biopharma and clinical unstructured data, with a special focus on extracting actionable intelligence from existing research and communications via Natural Language Processing (NLP).