Active Learning Solution on Distributed Edge Computing
This work addresses latency and privacy issues in Industry 4.0 by enabling efficient machine learning on distributed edge devices, though it is incremental as it builds on existing active and federated learning techniques.
The paper tackles the challenge of reducing data and communication costs in distributed edge computing by proposing a method that combines active learning on edge devices with federated learning on fog nodes, achieving significant reductions in data samples and communication overhead for image classification tasks.
Industry 4.0 becomes possible through the convergence between Operational and Information Technologies. All the requirements to realize the convergence is integrated on the Fog Platform. Fog Platform is introduced between the cloud server and edge devices when the unprecedented generation of data causes the burden of the cloud server, leading the ineligible latency. In this new paradigm, we divide the computation tasks and push it down to edge devices. Furthermore, local computing (at edge side) may improve privacy and trust. To address these problems, we present a new method, in which we decompose the data aggregation and 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 for an image classification task. In addition, we consider two settings: massively distributed and non-massively distributed and offer the corresponding solutions.