延迟受限边缘学习的流水线计算和通信优化
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
摘要：
考虑通过通信信道连接到边缘处理器的设备。该设备保存要卸载到边缘处理器的本地数据，以便训练机器学习模型，例如，用于回归或分类。数据到学习处理器的传输以及基于随机梯度下降（SGD）的训练都必须在时限内完成。假设通信和计算可以流水线，这封信调查的最佳选择的数据包有效载荷大小，给定的开销，每个数据包传输和计算和通信速率之间的比率。这相当于偏差和方差之间的权衡，因为首先传达整个数据集会减少训练过程的偏差，但可能不会留下足够的时间进行学习。解析界的预期最优性差距推导，从而使一个有效的优化，这是在数值结果中验证。
英文摘要：
Consider a device that is connected to an edge processor via a communication channel. The device holds local data that is to be offloaded to the edge processor so as to train a machine learning model, e.g., for regression or classification. Transmission of the data to the learning processor, as well as training based on Stochastic Gradient Descent (SGD), must be both completed within a time limit. Assuming that communication and computation can be pipelined, this letter investigates the optimal choice for the packet payload size, given the overhead of each data packet transmission and the ratio between the computation and the communication rates. This amounts to a tradeoff between bias and variance, since communicating the entire data set first reduces the bias of the training process but it may not leave sufficient time for learning. Analytical bounds on the expected optimality gap are derived so as to enable an effective optimization, which is validated in numerical results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
