Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in datacenters. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and civil society for privacy protection. 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 FL, despite being slower to converge in some cases, may result in a comparatively greener impact than a centralized equivalent setup. 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经常在数据中心进行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)等集中培训的替代办法。也许出乎意料的是,FL公司开始在全球规模部署,这些公司必须坚持来自政府和民间社会的新的保护隐私的法律要求和政策。然而,与FL公司有关的潜在环境影响仍然不明确且尚未探讨。本文首次对FL公司的碳足迹进行了系统研究。首先,我们提出了一个严格的模型,以量化碳足迹,从而便利对FL公司设计和碳排放之间的关系进行调查。然后,我们将FL公司的碳足迹与传统的集中学习进行比较。我们的研究结果表明,尽管在某些情况下,FL公司虽然比较缓慢地趋于一致,但可能会比集中的同等结构产生相对绿色的影响。我们在不同种类的数据集、环境以及各种深层次的学习模型中进行了广泛的实验。最后,我们强调了所报告的结果,并将之与FL公司未来的挑战和趋势联系起来,以降低其对环境的影响,包括更高的算法、硬件能力和趋势。