Training a machine learning model with federated edge learning (FEEL) is typically time-consuming due to the constrained computation power of edge devices and limited wireless resources in edge networks. In this paper, the training time minimization problem is investigated in a quantized FEEL system, where the heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing number of communication rounds. The training time is modeled by taking into account the communication time, computation time and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Further, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization via successive convex approximation and the subproblem of bandwidth allocation via bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the experimental results.
翻译:由于边缘设备的计算能力和边缘网络中无线资源的有限计算能力有限,因此机床学习模式通常耗时。在本文中,对培训时间最小化问题进行了量化的感知系统调查,在这种系统中,混杂的边缘设备通过正方形通道向边缘服务器发送四分化梯度。特别是,为压缩上传梯度采用了随机量化办法,这可以减少全方位通信的负担,但可能会以增加通信轮数的成本为代价。培训时间的模型考虑到通信时间、计算时间和通信轮数的有限计算能力。根据拟议的培训时间模型,对培训时间最小化问题进行了调查,根据拟议的最佳性向边缘服务器输送梯度梯度梯度的内在交易。此外,还提议采用联合数据与模型驱动的安装方法,以准确的最佳性差距为基础,据此计算通信轮数的封闭式搜索表达方式,并计算通信轮数的分配时间和通信轮数的总数。基于拟议培训时间的近端端间分配,通过不断优化的轨迹分析,根据拟议的最佳性水平,通过不断优化的升级的升级的系统进行。