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.

本论文提出了一种新的框架，用于分布式和联邦学习（FL）的能源和碳足迹分析，研究了完全分散方法和共识方法的能源足迹和碳排放。这个框架量化了基于连续改进的学习三个环节的能源消耗，使用了两个 5G 行业垂直的案例对其进行了分析，结果表明能源消耗和测试准确度应该通过模型和数据足迹来平衡。