Democratizing AI in Africa: FL for Low-Resource Edge Devices

TL;DR

Comparison 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 are demonstrated.

Abstract

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.

Figures (3)

• Figure 1: Fetal standard planes for the brain acquired at different centers. From left to right and from top to bottom, the planes are from Spain, Ghana, Egypt, Malawi, Algeria, Uganda
• Figure 2: Accuracy achieved with different datasets for Resnet-50
• Figure 3: Accuracy evolution during training of the centralized and federated models.