论文标题
在带宽受限网络中用于联合学习的滑动差分进化计划
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks
论文作者
论文摘要
在具有能量限制用户设备（UES）的带宽限制网络中的联合学习（FL）的探索量不足。在本文中，为了共同节省电池有限的UE所消耗的能源，并加快了FL中用于带宽有限网络的全球模型的融合，我们提出了基于滑动差分进化的调度（SDES）策略。为此，我们首先制定了一种优化，旨在最大程度地减少能源消耗的加权和模型训练收敛。然后，我们在几个小规模窗口中使用平行差分演化（DE）操作应用SDE，以有效地解决上述问题。与现有的调度策略相比，所提出的SDE在减少能耗和较低计算复杂性的模型收敛方面表现良好。
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.
