Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning

Understand

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.