without exchanging the data itself. Today's standard approach of centralizing data from multiple centers comes at the cost of critical concerns regarding patient privacy and data protection. To solve this problem, the ability to train machine learning models at scale across multiple medical institutions without moving the data is a critical technology. Nature Digital Medicine published the paper "The Future of Digital Health with Federated Learning" in September 2020, in which the authors explore how federated learning may provide a solution for the future of digital health, and highlight the challenges and considerations that need to be addressed. Recently, a collaboration of 20 different institutions around the world validated the utility of training AI models using federated learning. In a paper published in Nature Medicine "Federated learning for predicting clinical outcomes in patients with COVID-19", they showcased the accuracy and generalizability of a federated AI model for the prediction of oxygen needs in patients with COVID-19 infections. Furthermore, in a published paper "A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications", the authors trying to provide a set of challenges on FL challenges on medical data-centric perspective. Robotics Robotics includes a wide range of applications of machine learning methods: from perception and decision-making to control. As robotic technologies have been increasingly deployed from simple and repetitive tasks (e.g. repetitive manipulation) to complex and unpredictable tasks (e.g. autonomous navigation), the need for machine learning grows. Federated Learning provides a solution to improve over conventional machine learning training methods. In the paper, mobile robots learned navigation over diverse environments using the FL-based method, helping generalization. In the paper, Federated Learning is applied to improve multi-robot navigation under limited communication bandwidth scenarios, which is a current challenge in real-world learning-based robotic tasks. In the paper, Federated Learning is used to learn Vision-based navigation, helping better sim-to-real transfer. References External links "Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016" at eur-lex.europa.eu. Retrieved October 18, 2019. "Data minimisation and privacy-preserving techniques in AI systems" at UK Information Commissioners Office. Retrieved July 22, 2020 "Realising the Potential of Data Whilst Preserving Privacy with EyA and Conclave from R3" at eya.global. Retrieved March 31, 2022. Machine learning Distributed algorithms Multi-agent systems