  Recent advances in distributed learning raise environmental concerns due to
the large energy needed to train and move data to/from data centers. Novel
paradigms, such as federated learning (FL), are suitable for decentralized
model training across devices or silos that simultaneously act as both data
producers and learners. Unlike centralized learning (CL) techniques, relying on
big-data fusion and analytics located in energy hungry data centers, in FL
scenarios devices collaboratively train their models without sharing their
private data. This article breaks down and analyzes the main factors that
influence the environmental footprint of FL policies compared with classical
CL/Big-Data algorithms running in data centers. The proposed analytical
framework takes into account both learning and communication energy costs, as
well as the carbon equivalent emissions; in addition, it models both vanilla
and decentralized FL policies driven by consensus. The framework is evaluated
in an industrial setting assuming a real-world robotized workplace. Results
show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless
systems characterized by low bit/Joule efficiency (50 kbit/Joule or lower).
Consensus-driven FL does not require the parameter server and further reduces
emissions in mesh networks (200 kbit/Joule). On the other hand, all FL policies
are slower to converge when local data are unevenly distributed (often 2x
slower than CL). Energy footprint and learning loss can be traded off to
optimize efficiency.
