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Given the abundance and ease of access of personal data today, individualprivacy has become of paramount importance, particularly in the healthcaredomain. In this work, we aim to utilise patient data extracted from multiplehospital data centres to train a machine learning model without sacrificingpatient privacy. We develop a scheduling algorithm in conjunction with astudent-teacher algorithm that is deployed in a federated manner. This allows acentral model to learn from batches of data at each federal node. The teacheracts between data centres to update the main task (student) algorithm using thedata that is stored in the various data centres. We show that the scheduler,trained using meta-gradients, can effectively organise training and as a resulttrain a machine learning model on a diverse dataset without needing explicitaccess to the patient data. We achieve state-of-the-art performance and showhow our method overcomes some of the problems faced in the federated learningsuch as node poisoning. We further show how the scheduler can be used as amechanism for transfer learning, allowing different teachers to work togetherin training a student for state-of-the-art performance.

Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning