Democratizing AI in Africa: FL for Low-Resource Edge Devices
This addresses healthcare accessibility gaps in Africa by enabling AI model training on low-resource edge devices, though it is incremental in applying existing federated learning methods to a new domain.
This study tackled healthcare delivery challenges in Africa by using federated learning to train a fetal plane classifier with perinatal data from five African countries and Spain, demonstrating comparable performance to centralized models and improved generalizability despite computational limitations on low-resource devices like Raspberry Pis.
Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies. This study explores the use of federated learning to overcome these barriers, focusing on perinatal health. We trained a fetal plane classifier using perinatal data from five African countries: Algeria, Ghana, Egypt, Malawi, and Uganda, along with data from Spanish hospitals. To incorporate the lack of computational resources in the analysis, we considered a heterogeneous set of devices, including a Raspberry Pi and several laptops, for model training. We demonstrate comparative performance between a centralized and a federated model, despite the compute limitations, and a significant improvement in model generalizability when compared to models trained only locally. These results show the potential for a future implementation at a large scale of a federated learning platform to bridge the accessibility gap and improve model generalizability with very little requirements.