Equitable Federated Learning with NCA
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Federated Learning (FL) is enabling collaborative model training across
institutions without sharing sensitive patient data. This approach 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 system 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 medical imaging solutions,
fostering equitable healthcare advancements in resource-constrained regions.
