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Machine learning and wireless communication technologies are jointlyfacilitating an intelligent edge, where federated edge learning (FEEL) is apromising training framework. As wireless devices involved in FEEL are resourcelimited in terms of communication bandwidth, computing power and batterycapacity, it is important to carefully schedule them to optimize the trainingperformance. In this work, we consider an over-the-air FEEL system with analoggradient aggregation, and propose an energy-aware dynamic device schedulingalgorithm to optimize the training performance under energy constraints ofdevices, where both communication energy for gradient aggregation andcomputation energy for local training are included. The consideration ofcomputation energy makes dynamic scheduling challenging, as devices arescheduled before local training, but the communication energy for over-the-airaggregation depends on the l2-norm of local gradient, which is known afterlocal training. We thus incorporate estimation methods into scheduling topredict the gradient norm. Taking the estimation error into account, wecharacterize the performance gap between the proposed algorithm and its offlinecounterpart. Experimental results show that, under a highly unbalanced localdata distribution, the proposed algorithm can increase the accuracy by 4.9% onCIFAR-10 dataset compared with the myopic benchmark, while satisfying theenergy constraints.

Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints