Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored. In this paper, to jointly save energy consumed by the battery-limited UEs and accelerate the convergence of the global model in FL for the bandwidth-limited network, we propose the sliding differential evolution-based scheduling (SDES) policy. To this end, we first formulate an optimization that aims to minimize a weighted sum of energy consumption and model training convergence. Then, we apply the SDES with parallel differential evolution (DE) operations in several small-scale windows, to address the above proposed problem effectively. Compared with existing scheduling policies, the proposed SDES performs well in reducing energy consumption and the model convergence with lower computational complexity.

中文翻译：

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带宽受限网络中联合学习的滑动差分进化调度

在具有能量受限的用户设备 (UE) 的带宽受限网络中的联合学习 (FL) 尚未得到充分探索。在本文中，为了共同节省电池受限UE消耗的能量并加速FL中全局模型在带宽受限网络中的收敛，我们提出了基于滑动差分进化的调度（SDES）策略。为此，我们首先制定了一个优化方案，旨在最小化能耗和模型训练收敛的加权总和。然后，我们在几个小规模窗口中应用具有并行差分进化 (DE) 操作的 SDES，以有效解决上述问题。与现有的调度策略相比，所提出的 SDES 在降低能耗和模型收敛方面表现良好，计算复杂度较低。