Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and social groups advocating for privacy protection. \textit{However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL.} First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show that, depending on the configuration, FL can emit up to two order of magnitude more carbon than centralized machine learning. However, in certain settings, it can be comparable to centralized learning due to the reduced energy consumption of embedded devices. We performed extensive experiments across different types of datasets, settings and various deep learning models with FL. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.
翻译:尽管取得了令人印象深刻的成果,深层次的学习技术也引起了数据中心经常开展的培训程序引起的严重的隐私和环境关切。作为回应,出现了联邦学习联合会(FL)等集中培训的替代方法。也许出乎意料的是,FL公司开始在全球范围部署,这些公司必须坚持来自倡导隐私保护的政府和社会团体的新的法律要求和政策。\textit{然而,与FL有关的潜在环境影响仍然不明确且尚未探索。本文件首次对FL的碳足迹进行了系统研究。}首先,我们提出了一个严格的碳足迹量化模型,从而便利了FL设计与碳排放之间关系的调查。然后,我们将FL的碳足迹与传统的集中学习相比较。我们的调查结果显示,根据配置,FL的碳足迹比中央机器学习的碳含量高出两级以上。然而,在某些环境下,由于嵌入装置的能源消耗量减少,它可以与集中学习相比。我们在不同种类的数据集、环境设置和各种深层次学习模型中进行了广泛的实验,从而便利了FL设计与碳排放之间的关系。然后,我们将F的碳足迹与传统的集中学习过程的碳足迹与传统的学习关系加以比较。我们的碳足迹比,我们强调,并将环境趋势与所报告的环境影响与F的硬力与所报告的效率联系起来联系起来联系起来。最后,我们强调并联系,我们强调和报告的硬力与所报告的环境影响,并联系了环境上的透明度与所报告的结果。