The Role of Federated Learning in Processing Cancer Patients’ Data

Nowadays, the number of people living with cancer is constantly increasing. Numerous multidisciplinary research teams are working on development of powerful intelligent systems that will support medical decisions and help patients with critical diseases, including cancer, to maintain and even increase their quality of life (QoL). ASCAPE (Artificial intelligence Supporting CAncer Patients across Europe) is an H2020 project whose main objective is to use powerful techniques in big data, artificial intelligence, and machine learning in processing cancer (breast and prostate) patients’ data in order to support their health status. A key result of the project is the implementation of an artificial intelligence/machine learning (AI/ML) infrastructure. It will allow the deployment and execution of AI/ML algorithms locally in a hospital on patients’ private data, producing new knowledge. Newly generated knowledge will be sent back to the infrastructure and will be available to other users of the system keeping private patients’ data locally in hospitals. In this chapter, we will briefly present the structure of an open AI/ML infrastructure and how federated learning (FL) is employed in it.