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

Download PDF
Open Webpage

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

Published: 2025, Last Modified: 20 Mar 2026IEEE Big Data 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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 of labeled data. Specifically, we investigate the role of CutMix, a widely used patch-level augmentation technique, when combined with Federated FixMatch (FedFixMatch). We evaluate different strategies, including applying CutMix only to labeled data, only to unlabeled data, to both branches, or to mixed data, and examine their effects under varying annotation conditions and heterogeneous client distributions. Additionally, we examine the impact of CutMix on communication efficiency, convergence dynamics, and training stability within FedFixMatch. To the best of our knowledge, this work provides the first comprehensive benchmark of CutMix strategies in FSSL for medical image segmentation. Our results provide important guidance for developing efficient augmentation strategies that address the practical challenges of big data and FL.