Overcoming Non-IID Challenges in Federated Learning with Feature Learning
Published in Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 2025
Abstract. Federated Learning (FL) is a pivotal framework for enhancing AI models in healthcare by preserving data privacy during training. However, non-iid (not independent and identically distributed), due to diverse patient demographics and medical diagnostic tools, often hinders model convergence. To address this issue, we propose a novel approach that integrates an angular loss function to align feature representations across clients in angular space. Experimental results demonstrate that this method enhances the overall performance of FL strategies while improving the separation of feature representation of the global model. These findings underscore the potential for privacy-preserving AI applications in medicine.
Recommended citation: Phuong-Nam Tran, Seong-Bae Park, Choong Seon Hong. (2025). "Overcoming Non-IID Challenges in Federated Learning with Feature Learning." Proceedings of the Korean Institute of Information Scientists and Engineers (KIISE) Conference, 1578-1580.
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