Asynchronous and focal federated learning for skin lesion classification under local data scarcity and class imbalance

Federated learning (FL) is an approach that enables the training of machine learning (ML) models using data from multiple data nodes without direct data transfer, hence making it a good choice for healthcare ML applications to alleviate data privacy and security concerns. Most standard FL approaches focus on the setting of a small number of nodes, with each node contributing a sizable amount of data. However, in emerging healthcare settings such as telemedicine and the Internet of Medical Things (IoMT), it is necessary to consider the situation in which there is a large number of nodes, and each contributes a relatively small number (data scarcity) of non-independent (class imbalance) data points. In this paper, we propose an asynchronous and focal update approach to enable FL to address this problem. In particular, we demonstrate its use in a teledermatology setting, in which a skin lesion image classifier is continuously updated based on data in a highly distributed network of mobile devices. We performed a situation experiment in which 1,268 skin lesion images across 798 mobile devices contributed to the training of a 3-class classifier in an FL framework. We found that widely used synchronous FL methods perform poorly under conditions of data scarcity and imbalance. Specifically, using FedAvg, FedProx, and FedNova, the trained classifiers achieved AUROC values of 0.57-0.67, 0.63-0.66, and 0.64-0.67, respectively, on the held-out test set across various experimental settings. In contrast, our proposed asynchronous and focal approach achieved a test AUROC of 0.78-0.89 after 40 global training epochs. This performance is significantly closer to the optimal AUROC of 0.91, which is achievable by training a classifier with all the data on a centralised server without FL. These results demonstrate that our approach provides a useful solution to implement an efficient FL scheme under the conditions of data scarcity and class imbalance that are commonly found in realistic telemedicine and IoMT applications.