In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a learning algorithm by communicating learning updates with an access point without exchanging their data samples. With limited communication resources, it is beneficial to schedule the most informative local learning update. In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the importance of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

本文提出了一种新的调度策略，通过多用户通道和边缘设备学习更新的重要性方面的多样性来实现更快的模型收敛和更高的学习准确性，该策略在无线通信领域具有重要的实际应用价值。

具有重要性和通道感知的蜂窝联合边缘学习调度