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.

翻译：目的：FedSurg挑战赛旨在评估手术视频分类中联邦学习技术的当前水平。其目标是评估现有方法在未见临床中心的泛化能力，以及通过本地微调进行适应的效果，同时实现在不共享患者数据情况下的协作模型开发。方法：参赛者使用多中心Appendix300视频数据集的初步版本，开发了分类阑尾炎炎症阶段的策略。挑战赛评估了两项任务：对未见中心的泛化能力以及微调后的中心特定适应能力。提交的方法包括基于线性探测的基础模型、使用三元组损失的度量学习，以及多种联邦学习聚合方案（FedAvg、FedMedian、FedSAM）。性能通过F1分数和期望成本进行评估，并通过自助法和统计检验评估排名的稳健性。结果：在泛化任务中，各中心间的性能表现有限。在适应任务中，所有团队在微调后均有所改进，但排名稳定性较低。基于ViViT的提交方案取得了最强的整体性能。挑战赛揭示了泛化能力的局限性、对类别不平衡的敏感性以及去中心化训练中超参数调优的困难，而时空建模和上下文感知预处理则显示出作为有前景策略的潜力。结论：FedSurg挑战赛建立了首个用于评估手术视频分类中联邦学习策略的基准。研究结果凸显了本地个性化与全局鲁棒性之间的权衡，并强调了架构选择、预处理和损失函数设计的重要性。该基准为临床手术人工智能中未来开发不平衡感知、自适应且鲁棒的联邦学习方法提供了参考依据。