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Distributed and Centralized Options for Self-Learning

In general, the availability of enough real data from real fog computing scenarios to produce accurate Machine Learning (ML) models is rarely ensured since new equipment, techniques, etc., are continuously being deployed in the field. Although an option is to generate data from simulation and lab experiments, such data could not cover the whole features space, which would translate into ML models inaccuracies. In this paper, we propose a self-learning approach to facilitate ML deployment in real scenarios. A dataset for ML training can be initially populated based on the results from simulation and lab experiments and once ML models are generated, ML re-training can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits from the proposed self-learning approach for two general use cases of regression and classification.