Distributed Active Learning Strategies on Edge Computing
Jia Qian, Sarada Prasad Gochhayat, Lars Kai Hansen · 2019
Fog platform brings the computing power from the remote cloud-side closer to the edge devices to reduce latency, as the unprecedented generation of data causes ineligible latency to process the data in a centralized fashion at the Cloud. In this new setting, edge devices with distributed computing capability, such as sensors, surveillance camera, can communicate with fog nodes with less latency. Furthermore, local computing (at edge side) may improve privacy and trust. In this paper, we present a new method, in which, we decompose the data processing, by dividing them between edge devices and fog nodes, intelligently. We apply active learning on edge devices; and federated learning on the fog node which significantly reduces the data samples to train the model as well as the communication cost. To show the effectiveness of the proposed method, we implemented and evaluated its performance on a benchmark images data set.