Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data
This work addresses scalability and adoption challenges for Edge AI/ML in sectors like IoT and mobile, but it appears incremental as it builds on existing decentralized learning concepts.
The paper tackles the problem of scaling machine learning on decentralized data from edge devices by addressing adoption barriers, proposing a model-centric design that treats trained models as commodities to enable efficient collaborative learning at scale.
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.