  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, 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 the 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 FL, despite being slower to converge, can be a
greener technology than data center GPUs. 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.
