# FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity Source: [FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:57:11.769Z Authors: Zhaobin Sun, Nannan Wu, Junjie Shi, Li Yu, Xin Yang, Kwang-Ting Cheng, Zengqiang Yan (1) \_\_INSTITUTION\_7\_\_ School of Electronic Information, Communications, Huazhong University of Science, Technology, (2) School of Engineering, Hong Kong University of Science, Technology) Published: 2024-06-27T08:36:43.000Z ## Abstract Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical image classification. One common assumption is task homogeneity where each client has access to all classes during training. However, in clinical practice, given a multi-label classification task, constrained by the level of medical knowledge and the prevalence of diseases, each institution may diagnose only partial categories, resulting in task heterogeneity. How to pursue effective multi-label medical image classification under task heterogeneity is under-explored. In this paper, we first formulate such a realistic label missing setting in the multi-label FL domain and propose a two-stage method FedMLP to combat class missing from two aspects: pseudo label tagging and global knowledge learning. The former utilizes a warmed-up model to generate class prototypes and select samples with high confidence to supplement missing labels, while the latter uses a global model as a teacher for consistency regularization to prevent forgetting missing class knowledge. Experiments on two publicly-available medical datasets validate the superiority of FedMLP against the state-of-the-art both federated semi-supervised and noisy label learning approaches under task heterogeneity. Code is available at https://github.com/szbonaldo/FedMLP. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()