Equitable Federated Learning with NCA

Nick Lemke, Mirko Konstantin, Henry John Krumb, John Kalkhof, Jonathan Stieber, Anirban Mukhopadhyay

Abstract:

Federated Learning (FL) is enabling collaborative model train-
ing across institutions without sharing sensitive patient data. This ap-
proach is particularly valuable in low- and middle-income countries (LMICs),
where access to trained medical professionals is limited. However, FL
adoption in LMICs faces significant barriers, including limited high-
performance computing resources and unreliable internet connectivity.
To address these challenges, we introduce FedNCA, a novel FL sys-
tem tailored for medical image segmentation tasks. FedNCA leverages
the lightweight Med-NCA architecture, enabling training on low-cost
edge devices, such as widely available smartphones, while minimizing
communication costs. Additionally, our encryption-ready FedNCA
proves to be suitable for compromised network communication.
By overcoming infrastructural and security challenges, FedNCA paves
the way for inclusive, efficient, lightweight, and encryption-ready med-
ical imaging solutions, fostering equitable healthcare advancements in
resource-constrained regions