论文标题
无线分布式边缘学习：我们需要多少个边缘设备？
Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?
论文作者
论文摘要
我们考虑在无线边缘的分布式机器学习，其中参数服务器借助多个无线边缘设备来构建全局模型，这些设备在本地数据集分区上执行计算。 Edge设备使用固定速率和正交多访问权限，将其计算结果（当前全局模型的更新）传输到服务器上。如果发生变速箱误差，则将未拨动的数据包重新启动，直到在接收器上成功解码为止。利用分布式系统中计算和通信之间的基本权衡，我们的目的是得出需要多少个边缘设备，以最大程度地减少平均完成时间，同时保证收敛。我们为平均完成提供了上限和下限，并找到了在两个渐近方案中添加边缘设备的必要条件，即大数据集和高精度机制。对实际数据集进行了实验，数值结果证实了我们的分析，并证实了我们的主张，即应仔细选择边缘设备的数量以及时分布式边缘学习。
We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning.
