Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on
  Decentralized Data
Ahmed M. Abdelmoniem
With mobile, IoT and sensor devices becoming pervasive in our life and recent
advances in Edge Computational Intelligence (e.g., Edge AI/ML), it became
evident that the traditional methods for training AI/ML models are becoming
obsolete, especially with the growing concerns over privacy and security. This
work tries to highlight the key challenges that prohibit Edge AI/ML from seeing
wide-range adoption in different sectors, especially for large-scale scenarios.
Therefore, we focus on the main challenges acting as adoption barriers for the
existing methods and propose a design with a drastic shift from the current
ill-suited approaches. The new design is envisioned to be model-centric in
which the trained models are treated as a commodity driving the exchange
dynamics of collaborative learning in decentralized settings. It is expected
that this design will provide a decentralized framework for efficient
collaborative learning at scale.