  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.
