Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentation
This work addresses the detrimental impact of communication noise on training stability and accuracy in SplitFed learning for medical image segmentation. To mitigate this issue, the authors propose an intelligent averaging strategy that enhances model robustness against highly noisy communication channels within the Split-Federated learning framework. The proposed method maintains high segmentation accuracy while tolerating communication noise levels up to two orders of magnitude greater than those manageable by conventional averaging mechanisms. Consequently, it significantly improves both the stability and performance of the system under adverse channel conditions, enabling more reliable deployment of SplitFed learning in real-world medical imaging applications where communication quality may be limited.