A Carbon Tracking Model for Federated Learning: Impact of Quantization
  and Sparsification
Luca Barbieri Stefano Savazzi Sanaz Kianoush Monica Nicoli Luigi Serio
Federated Learning (FL) methods adopt efficient communication technologies to
distribute machine learning tasks across edge devices, reducing the overhead in
terms of data storage and computational complexity compared to centralized
solutions. Rather than moving large data volumes from producers (sensors,
machines) to energy-hungry data centers, raising environmental concerns due to
resource demands, FL provides an alternative solution to mitigate the energy
demands of several learning tasks while enabling new Artificial Intelligence of
Things (AIoT) applications. This paper proposes a framework for real-time
monitoring of the energy and carbon footprint impacts of FL systems. The carbon
tracking tool is evaluated for consensus (fully decentralized) and classical FL
policies. For the first time, we present a quantitative evaluation of different
computationally and communication efficient FL methods from the perspectives of
energy consumption and carbon equivalent emissions, suggesting also general
guidelines for energy-efficient design. Results indicate that consensus-driven
FL implementations should be preferred for limiting carbon emissions when the
energy efficiency of the communication is low (i.e., < 25 Kbit/Joule). Besides,
quantization and sparsification operations are shown to strike a balance
between learning performances and energy consumption, leading to sustainable FL
designs.