Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data

TL;DR

This paper discusses the challenges of deploying scalable, privacy-preserving machine learning on decentralized edge devices and proposes a model-centric framework for collaborative learning across the edge-to-cloud continuum.

Contribution

It introduces a novel decentralized, model-centric framework that addresses key barriers to large-scale Edge AI/ML adoption, emphasizing collaborative learning.

Findings

Identifies key challenges hindering Edge AI/ML scalability.

Proposes a new model-centric design for decentralized collaborative learning.

Envisions a framework enabling efficient, large-scale Edge AI/ML deployment.

Abstract

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.