GDI Pillar III aims to explore use cases and innovative applications for analysing genomic and clinical data, ideally supported by the infrastructure being deployed at the national nodes within Pillar II. Two recent trends can be observed in this area: the application of the FAIR principles computational workflows for enabling scalable and reproducible analyses and the increasing application of artificial intelligence techniques . Federated learning is described as a distributed machine learningtechnique in which multiple participants, which provide remote devices or siloed data centres, collaboratively train a shared machine learning model while keeping their data locally, better supporting data privacy. As the model is trained locally by each participant on its own data, only model updates (e.g. gradients or weights) are sent to a central server. The central server aggregates these updates to improve the global model, which is then sent back to the participants for further iterative training rounds. Therefore, federated learning enables collaborative learning from distributed data sources without sharing the original data, thus reducing privacy concerns andleveraging the aggregate knowledge available to the multiple participants. In this report, we provide a brief background on recent work on federated learning applied to genomics and health and how they are aligned to demonstrations performed in GDI, report the results of surveys conducted within the GDI participants regarding workflows and federated learning technologies, and discuss and evaluation of different possible scenarios for integrating these technologies into the GDI infrastructure.