Distributed Learning Technique with Deep ESN-Based Models for Energy Forecasting

Advances in machine learning (ML) have opened up new opportunities to include decision-making capabilities in Internet of Things (IoT) nodes. This opportunity is complex to address, since conventional ML implementations are computationally intensive, thus reducing the possibility of implementing them on resource-constrained systems. In addition, when nodes in a network grow significantly, centralized data processing creates new challenges such as latency, resource efficiency, privacy, bandwidth, etc. Aiming to simultaneously address the above challenges, this paper presents a novel efficient distributed learning strategy for ESN-Based model, that is conceived for training and inference to incorporate part of the model while reducing the sharing of private information. The experiment setup was conducted using Appliances Energy Prediction dataset to forecast energy consumption under different environmental and usage conditions, using simulated IoT devices. The numerical results show that the proposed solution performs well compared to a classical centralized ESN-based approach without sacrificing too much performance at very low computational costs.