  The rapid progress of AI is fueled by increasingly large and computationally
intensive machine learning models and datasets. As a consequence, the amount of
compute used in training state-of-the-art models is exponentially increasing
(doubling every 10 months between 2015 and 2022), resulting in a large carbon
footprint. Federated Learning (FL) - a collaborative machine learning technique
for training a centralized model using data of decentralized entities - can
also be resource-intensive and have a significant carbon footprint,
particularly when deployed at scale. Unlike centralized AI that can reliably
tap into renewables at strategically placed data centers, cross-device FL may
leverage as many as hundreds of millions of globally distributed end-user
devices with diverse energy sources. Green AI is a novel and important research
area where carbon footprint is regarded as an evaluation criterion for AI,
alongside accuracy, convergence speed, and other metrics. In this paper, we
propose the concept of Green FL, which involves optimizing FL parameters and
making design choices to minimize carbon emissions consistent with competitive
performance and training time. The contributions of this work are two-fold.
First, we adopt a data-driven approach to quantify the carbon emissions of FL
by directly measuring real-world at-scale FL tasks running on millions of
phones. Second, we present challenges, guidelines, and lessons learned from
studying the trade-off between energy efficiency, performance, and
time-to-train in a production FL system. Our findings offer valuable insights
into how FL can reduce its carbon footprint, and they provide a foundation for
future research in the area of Green AI.
