FedCarbon: Carbon-Efficient Federated Learning with Double Flexible Controls for Green Edge AI
Yousheng Li, Tao Ouyang, Xu Chen, Xinyuan Cao · 2024
The deep integration of federated learning (FL) and edge computing holds great promise in delivering ubiquitous edge AI services. However, in light of the upcoming carbon peaking and neutrality era, existing research has largely overlooked the sustainability challenges of FL in future edge computing. Therefore, we first propose a novel carbon-efficient FL framework in this paper, which leverages client sampling and model pruning approaches to adjust carbon-aware local model training during the long-term FL procedure, adapt to heterogeneous and dynamic edge environments, such as time-varying renewable energy and edge workloads. We then conduct a theoretical analysis of its convergence bound, based on which we introduce an online control algorithm to efficiently balance the trade-off between training performance and carbon emission, i.e., maximizing carbon efficiency while ensuring satisfactory FL performance. The effectiveness of proposed algorithm is verified by extensive trace-driven simulations, reducing up-to 72% carbon emissions than other methods.