Federated Semi-Supervised FixMatch: Enhancing CutMix for Medical Image Segmentation

Abstract (excerpt). Previous studies on federated learning (FL) often assume that each client has access to fully labeled data. In reality, however, most hospitals and clinical facilities lack sufficient labeled data, as annotation is costly and requires highly skilled experts. The key challenge, therefore, is how to effectively train FL models when only a small portion of data at each client is labeled. Federated semi-supervised learning (FSSL) has emerged as a promising solution. From the perspective of big data, this challenge is further intensified by the large scale, heterogeneity, and non-IID nature of data distributions. These factors significantly affect training efficiency, making it crucial to fully leverage diverse unlabeled datasets while ensuring privacy and minimizing communication costs for large-scale FL deployment. In this study, we apply data augmentation in FSSL to make better use of unlabeled data together with the limited amount …

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Recommended citation: Thu Thuy Le, Nhut Minh Nguyen, Nhat Truong Pham, Phuong-Nam Tran, Nguyen Doan Hieu Nguyen, Phuong Luu Vo, Balachandran Manavalan, Duc Ngoc Minh Dang. (2025). "Federated Semi-Supervised FixMatch: Enhancing CutMix for Medical Image Segmentation." 2025 IEEE International Conference on Big Data (BigData), 3462-3471.
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