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

• URL: http://arxiv.org/abs/2306.10848v1
• Date: Mon, 19 Jun 2023 10:59:41 GMT
• Title: Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data
• Authors: Ahmed M. Abdelmoniem
• Abstract summary: This work tries to highlight the key challenges that prohibit Edge AI/ML from seeing wide-range adoption. The new design is envisioned to be model-centric in which the trained models are treated as a commodity. It is expected that this design will provide a decentralized framework for efficient collaborative learning at scale.
• Score: 3.448338949969246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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