COLLECTIVE SELF-LEARNING BY EXCHANGING ML MODELS

Collective self-learning based on Machine Learning (ML) model sharing and combination is proposed to accelerate ML-based algorithm deployment. The considered architecture is presented, together with three different alternatives for combining ML models. Performance analysis is carried out on an illustrative use case for autonomic transmission.