Federated learning (FL) enables collaborative model training over privacy-sensitive, distributed data, but its environmental impact is difficult to compare across studies due to inconsistent measurement boundaries and heterogeneous reporting. We present a practical carbon-accounting methodology for FL CO2e tracking using NVIDIA NVFlare and CodeCarbon for explicit, phase-aware tasks (initialization, per-round training, evaluation, and idle/coordination). To capture non-compute effects, we additionally estimate communication emissions from transmitted model-update sizes under a network-configurable energy model. We validate the proposed approach on two representative workloads: CIFAR-10 image classification and retinal optic disk segmentation. In CIFAR-10, controlled client-efficiency scenarios show that system-level slowdowns and coordination effects can contribute meaningfully to carbon footprint under an otherwise fixed FL protocol, increasing total CO2e by 8.34x (medium) and 21.73x (low) relative to the high-efficiency baseline. In retinal segmentation, swapping GPU tiers (H100 vs.\ V100) yields a consistent 1.7x runtime gap (290 vs. 503 minutes) while producing non-uniform changes in total energy and CO2e across sites, underscoring the need for per-site and per-round reporting. Overall, our results support a standardized carbon accounting method that acts as a prerequisite for reproducible 'green' FL evaluation. Our code is available at https://github.com/Pediatric-Accelerated-Intelligence-Lab/carbon_footprint.

翻译：联邦学习（FL）可在隐私敏感的分布式数据上实现协作模型训练，但由于测量边界不一致及报告方式各异，其环境影响难以跨研究进行比较。我们提出了一种实用的碳核算方法，用于跟踪FL的CO2e排放：该方法基于NVIDIA NVFlare和CodeCarbon，明确划分阶段化任务（初始化、每轮训练、评估及空闲/协调阶段）。为捕捉非计算效应，我们进一步通过可配置网络能耗模型，根据传输的模型更新大小估算通信排放。我们在两项代表性任务（CIFAR-10图像分类与视网膜视盘分割）上验证了所提方法。在CIFAR-10任务中，受控客户端效率场景表明：在固定FL协议下，系统级降速与协调效应可能显著影响碳足迹——相较于高效率基线，中等效率与低效率场景的总CO2e分别增加8.34倍和21.73倍。在视网膜分割任务中，GPU层级切换（H100 vs. V100）导致运行时一致偏差1.7倍（290分钟 vs. 503分钟），同时各站点总能耗与CO2e变化呈现非均匀性，凸显了按站点与按轮次报告的必要性。总体而言，我们的研究结果支持一种标准化碳核算方法，该方法是可复现的“绿色”FL评估的前提。代码开源于：https://github.com/Pediatric-Accelerated-Intelligence-Lab/carbon_footprint