Abstract
Purpose: The FedSurg challenge was designed to benchmark the state of the art in federated learning for surgical video classification. Its goal was to assess how well current methods generalize to unseen clinical centers and adapt through local fine-tuning while enabling collaborative model development without sharing patient data. Methods: Participants developed strategies to classify inflammation stages in appendicitis using a preliminary version of the multi-center Appendix300 video dataset. The challenge evaluated two tasks: generalization to an unseen center and center-specific adaptation after fine-tuning. Submitted approaches included foundation models with linear probing, metric learning with triplet loss, and various FL aggregation schemes (FedAvg, FedMedian, FedSAM). Performance was assessed using F1-score and Expected Cost, with ranking robustness evaluated via bootstrapping and statistical testing. Results: In the generalization task, performance across centers was limited. In the adaptation task, all teams improved after fine-tuning, though ranking stability was low. The ViViT-based submission achieved the strongest overall performance. The challenge highlighted limitations in generalization, sensitivity to class imbalance, and difficulties in hyperparameter tuning in decentralized training, while spatiotemporal modeling and context-aware preprocessing emerged as promising strategies. Conclusion: The FedSurg Challenge establishes the first benchmark for evaluating FL strategies in surgical video classification. Findings highlight the trade-off between local personalization and global robustness, and underscore the importance of architecture choice, preprocessing, and loss design. This benchmarking offers a reference point for future development of imbalance-aware, adaptive, and robust FL methods in clinical surgical AI.
Abstract (translated)
目的：FedSurg挑战赛旨在评估联邦学习在手术视频分类领域的最新成果。其目标是衡量现有方法推广到未见过的临床中心的能力，以及通过本地微调适应这些新环境的能力，并且能够在不分享患者数据的情况下促进合作模型开发。 方法：参赛者利用多中心Appendix300视频数据集的一个初步版本，开发了用于分类阑尾炎炎症阶段的策略。挑战赛评估了两项任务：推广到未见过的中心和在微调后的特定中心适应性。提交的方法包括带有线性探测的基础模型、使用三元组损失的度量学习以及各种FL聚合方案（FedAvg、FedMedian、FedSAM）。性能通过F1分数和预期成本进行评估，而排名稳健性的评估则通过自助法和统计检验来进行。 结果：在推广任务中，跨中心的性能有限。在适应性任务中，所有团队在微调后都有所改进，尽管排名稳定性较低。基于ViViT的方法取得了最强的整体表现。该挑战赛突显了推广能力、对类别不平衡敏感度以及去中心化训练中超参数调整困难等限制，并且时空建模和上下文感知预处理被视为有前景的策略。 结论：FedSurg挑战赛建立了首个用于评估手术视频分类中FL策略的基准测试。研究结果强调了本地个性化与全局稳健性之间的权衡，突出了架构选择、预处理和损失设计的重要性。这一基准为未来开发针对不平衡、适应性和稳健性的临床外科AI FL方法提供了参考点。
URL
https://arxiv.org/abs/2510.04772
