Unified Federated Partially Labeled Segmentation

Unified Federated Partially Labeled Segmentation aims to train accurate image segmentation models across multiple decentralized datasets (federated learning) where only some data is labeled, addressing privacy and data scarcity challenges. Research focuses on adapting architectures like U-Net and fully convolutional networks (FCNs) to handle partial labels and heterogeneous data distributions, often employing techniques like pseudo-labeling and sharpness-aware minimization to improve model performance. This approach is particularly relevant for applications like medical imaging and autonomous driving, where labeled data is expensive and privacy is paramount, enabling the development of robust models without compromising sensitive information.