Federated Neural Architecture Search with Hierarchical Progressive Acceleration for Medical Image Segmentation

Deep neural networks for medical image segmentation often require data from multiple medical institutions, but privacy concerns limit data sharing, making federated learning (FL) a viable alternative. However, predefined network architectures in FL are often suboptimal and need extensive manual tuning. Traditional neural architecture search (NAS) methods are unsuitable for FL due to high communication and evaluation costs. This paper presents an evolutionary NAS method (FS-ENAS) for federated medical image segmentation. FS-ENAS utilizes a UNet++ based supernet with depthwise separable convolution and adaptable skip connections. It introduces a novel multi-stage, hierarchical progressive acceleration strategy tailored for federated neural architecture search to reduce communication and evaluation burdens. Experimental results on retinal blood vessel segmentation tasks show that FS-ENAS efficiently searches for suitable architectures with reduced communication and evaluation costs while protecting privacy.