Dynamic Resource Allocation For Wireless Edge Machine Learning With Latency And Accuracy Guarantees

In this paper, we address the problem of dynamic allocation of communication and computation resources for Edge Machine Learning (EML) exploiting Multi-Access Edge Computing (MEC). In particular, we consider an IoT scenario, where sensor devices collect data from the environment and upload them to an edge server that runs a learning algorithm based on Stochastic Gradient Descent (SGD). The aim is to explore the optimal tradeoff between the overall system energy consumption, including IoT devices and edge server, the overall service latency, and the learning accuracy. Building on stochastic optimization tools, we devise an algorithm that jointly allocates radio and computation resources in a dynamic fashion, without requiring prior knowledge of the statistics of the channels, task arrivals, and input data. Finally, we test our algorithm in the specific case the edge server runs a Least Mean Squares (LMS) algorithm on the data acquired by each sensor device.