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In Federated Learning (FL), a global statistical model is developed byencouraging mobile users to perform the model training on their local data andaggregating the output local model parameters in an iterative manner. However,due to limited energy and computation capability at the mobile devices, theperformance of the model training is always at stake to meet the objective oflocal energy minimization. In this regard, Multi-access Edge Computing(MEC)-enabled FL addresses the tradeoff between the model performance and theenergy consumption of the mobile devices by allowing users to offload a portionof their local dataset to an edge server for the model training. Since the edgeserver has high computation capability, the time consumption of the modeltraining at the edge server is insignificant. However, the time consumption fordataset offloading from mobile users to the edge server has a significantimpact on the total time consumption. Thus, resource management in MEC-enabledFL is challenging, where the objective is to reduce the total time consumptionwhile saving the energy consumption of the mobile devices. In this paper, weformulate an energy-aware resource management for MEC-enabled FL in which themodel training loss and the total time consumption are jointly minimized, whileconsidering the energy limitation of mobile devices. In addition, we recast theformulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capturethe coupling constraints between the radio resource management and datasetoffloading. We then analyze the impact of the dataset offloading and computingresource allocation on the model training loss, time, and the energyconsumption.

Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems