Testing Federated Learning on Health and Wellbeing Data

Nowadays, artificial intelligence is used in healthcare applications, too. Based on current research, personalized medicine could transform the healthcare domain. Thus, user medical data must be collected and used for training models. To preserve the privacy of data, federated learning represents a good candidate. This paper proposes an extension of the federated learning model that is evaluated for learning over a distributed dataset. The proposed architecture is a client-server, where the clients are clustered by the server according to their data similarity (without exposing data to the server). The server stores the cluster models and manages the clients. Different tests were performed on three datasets: CIFAR-10, MNIST, and a non-standard one - a sleep dataset. Results show that an increase in the convergence rate was obtained (in the case of the MNIST dataset, it was 50 times faster). Also, the method can learn patterns from the data by keeping data locally.