Green concerns in federated learning over 6G

Abstract

As Information, Communications, and Data Technology (ICDT) are deeply integrated, the research of 6G gradually rises. Meanwhile, federated learning (FL) as a distributed artificial intelligence (AI) framework is generally believed to be the most promising solution to achieve "Native AI" in 6G. While the adoption of energy as a metric in AI and wireless networks is emerging, most studies still focused on obtaining high levels of accuracy, with little consideration on new features of future networks and their possible impact on energy consumption. To address this issue, this article focuses on green concerns in FL over 6G. We first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks, and model the energy consumption in FL over 6G from aspects of computation and communication. We classify and summarize the basic ways to reduce energy, and present several feasible green designs for FL-based 6G network architecture from three perspectives. According to the simulation results, we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.