BrainFL: Federated learning for brain diseases classification
DOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE9.2025.101-110Keywords:
Federated learning; Heterogeneous clients; Brain disease classification.Abstract
In the healthcare domain, data privacy is a critical concern. As a result, a recent strategy for training AI models for healthcare-related problems is federated learning (FL), where models are trained locally at the clients, and only their encoded weights are sent to a central server for aggregation. However, data collected from various clinics or organizations may differ significantly in terms of quality, quantity, and distribution. In addition, the availability of clients participating in the training process is often inconsistent. Furthermore, the characteristics of specific diseases may strongly influence the performance of FL algorithms. In this paper, we focus on the classification of brain diseases using medical images. We design a framework, namely called BrainFL, to investigate several FL algorithms (FedAvg, FedNH, and FedProto), coupled with lightweight CNNs (two custom-designed networks named BrainCNN-2 and BrainCNN-4, and the standard ResNet-18) for feature extraction and classification. Our objective is to evaluate three key factors that significantly impact the overall performance of the task: local data distribution, client availability, and disease variation. Experiments are conducted on two benchmarks of brain disease images: ICH and Brain Tumors, under various experimental settings (e.g., client participation rates and levels of non-IID data). The results reveal valuable insights for deploying such FL frameworks in practical applications.
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