  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.
