Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09496 (cs)
[Submitted on 10 Mar 2026]
Title:SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding
Authors:Zheng Fang, Ziwei Niu, Ziyue Wang, Zhu Zhuo, Haofeng Liu, Shuyang Qian, Jun Xia, Yueming Jin
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Abstract:Surgical scene Multi-Task Federated Learning (MTFL) is essential for robot-assisted minimally invasive surgery (RAS) but remains underexplored in surgical video understanding due to two key challenges: (1) Tissue Diversity: Local models struggle to adapt to site-specific tissue features, limiting their effectiveness in heterogeneous clinical environments and leading to poor local predictions. (2) Task Diversity: Server-side aggregation, relying solely on gradient-based clustering, often produces suboptimal or incorrect parameter updates due to inter-site task heterogeneity, resulting in inaccurate localization. In light of these two issues, we propose SurgFed, a multi-task federated learning framework, enabling federated learning for surgical scene segmentation and depth estimation across diverse surgical types. SurgFed is powered by two appealing designs, i.e., Language-guided Channel Selection (LCS) and Language-guided Hyper Aggregation (LHA), to address the challenge of fully exploration on corss-site and cross-task. Technically, the LCS is first designed a lightweight personalized channel selection network that enhances site-specific adaptation using pre-defined text inputs, which optimally the local model learn the specific embeddings. We further introduce the LHA that employs a layer-wise cross-attention mechanism with pre-defined text inputs to model task interactions across sites and guide a hypernetwork for personalized parameter updates. Extensive empirical evidence shows that SurgFed yields improvements over the state-of-the-art methods in five public datasets across four surgical types. The code is available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09496 [cs.CV] |
| (or arXiv:2603.09496v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09496
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View a PDF of the paper titled SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding, by Zheng Fang and 7 other authors
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