  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.
