DescriptionThe inundation of data generation technologies, along with progress in Artificial Intelligence (AI) and increasing privacy concerns, has prompted research into techniques for both federated and privacy preserving AI. Federated Learning (FL) allows multiple clients to collaborate in training AI models without sharing data while privacy preserving AI places further emphasis on protecting client data. To date, federated and privacy preserving AI are primarily driven by consumer demand for fast and accurate analysis on personal devices which may contain sensitive data. In the HPC domain, FL interest has grown in the areas of health analytics and coordination across experimental facilities. This latter form of FL poses new and unsolved problems. This workshop aims to highlight research in all aspects of federated and privacy preserving AI for HPC, machine learning, and scientific participants. Broad goals would be to consolidate the community around a core set of objectives and foster new collaborations.