Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Only Look Once (YOLO) is challenged by limited data, privacy constraints, and inter-institutional variability. Federated learning (FL) enables collaborative training without sharing raw data, yet practical support for modern YOLO pipelines under heterogeneous surgical data remains limited. We present UltraFlwr, an open-source, communication-efficient, and edge-deployable framework that integrates Ultralytics YOLO with the Flower FL platform and supports native Partial Aggregation (PA) of YOLO components (backbone, neck, head). Using two public laparoscopic surgical tool detection datasets, we conduct a systematic empirical study of federated YOLO training under Independent and Identically Distributed (IID) and multiple clinically motivated heterogeneous scenarios, including differences in data curation, video length, and label availability. Results show that standard FL aggregators (e.g., FedAvg) do not consistently match centralized training per client, but reduce inter-client performance variability. Aggregating both backbone and neck components achieves performance comparable to full aggregation with lower communication costs. Also, improving within-client data consistency can benefit FL even when it increases distribution shift across clients. These findings provide practical guidance for deploying federated YOLO-based object detection in heterogeneous surgical environments. UltraFlwr is publicly available at https://github.com/KCL-BMEIS/UltraFlwr.

翻译：腹腔镜视频中的手术目标检测能够为工作流分析和技能评估提供实时器械识别，但训练鲁棒模型（如YOLO）面临数据有限、隐私约束及机构间差异等挑战。联邦学习（FL）支持在不共享原始数据的情况下进行协同训练，然而针对异构手术数据下现代YOLO流程的实际支持仍然有限。本文提出UltraFlwr——一个开源、通信高效且可边缘部署的框架，该框架将Ultralytics YOLO与Flower联邦学习平台集成，并支持对YOLO组件（骨干网络、颈部网络、头部网络）进行原生部分聚合（PA）。基于两个公开的腹腔镜手术工具检测数据集，我们在独立同分布（IID）及多种临床驱动的异构场景（包括数据标注差异、视频长度差异和标签可用性差异）下，对联邦YOLO训练进行了系统性实证研究。结果表明：标准联邦聚合算法（如FedAvg）虽不能始终达到各客户端独立集中训练的性能，但能降低客户端间的性能波动；同时聚合骨干网络与颈部网络可在降低通信成本的同时，获得与完全聚合相当的性能；此外，提升客户端内部数据一致性即使可能加剧客户端间的分布差异，仍对联邦学习具有积极影响。这些发现为在异构手术环境中部署基于联邦YOLO的目标检测提供了实用指导。UltraFlwr已在https://github.com/KCL-BMEIS/UltraFlwr 公开。