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.

基于边缘设备的联邦学习方法通过采用高效的通信技术来分布机器学习任务，相比于集中式解决方案，减少了数据存储和计算复杂性方面的开销。该论文提出了一个用于实时监测联邦学习系统能源和碳足迹影响的框架，并通过对共识（完全分散）和经典联邦学习策略的评估，首次定量评估了不同的计算和通信高效联邦学习方法在能源消耗和碳等效排放方面的表现，并提供了能效设计的一般指导原则。研究结果表明，在通信能效较低（即 < 25 Kbit/Joule）时，应优先选择共识驱动的联邦学习实现以限制碳排放。此外，量化和稀疏化操作被证明在学习性能和能源消耗之间达到了平衡，从而导致了可持续的联邦学习设计。