An Energy and Carbon Footprint Analysis of Distributed and Federated
Learning
- URL: http://arxiv.org/abs/2206.10380v1
- Date: Tue, 21 Jun 2022 13:28:49 GMT
- Title: An Energy and Carbon Footprint Analysis of Distributed and Federated
Learning
- Authors: Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush, Mehdi Bennis
- Abstract summary: Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers.
Emerging alternatives to mitigate such high energy costs propose to efficiently distribute, or federate, the learning tasks across devices.
This paper proposes a novel framework for the analysis of energy and carbon footprints in distributed and federated learning.
- Score: 42.37180749113699
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Classical and centralized Artificial Intelligence (AI) methods require moving
data from producers (sensors, machines) to energy hungry data centers, raising
environmental concerns due to computational and communication resource demands,
while violating privacy. Emerging alternatives to mitigate such high energy
costs propose to efficiently distribute, or federate, the learning tasks across
devices, which are typically low-power. This paper proposes a novel framework
for the analysis of energy and carbon footprints in distributed and federated
learning (FL). The proposed framework quantifies both the energy footprints and
the carbon equivalent emissions for vanilla FL methods and consensus-based
fully decentralized approaches. We discuss optimal bounds and operational
points that support green FL designs and underpin their sustainability
assessment. Two case studies from emerging 5G industry verticals are analyzed:
these quantify the environmental footprints of continual and reinforcement
learning setups, where the training process is repeated periodically for
continuous improvements. For all cases, sustainability of distributed learning
relies on the fulfillment of specific requirements on communication efficiency
and learner population size. Energy and test accuracy should be also traded off
considering the model and the data footprints for the targeted industrial
applications.
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