Towards a Decentralized Collaborative Framework for Scalable Edge AI

Ahmed M. Abdelmoniem (), Mona Jaber, Ali Anwar, Yuchao Zhang and Mingliang Gao
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Ahmed M. Abdelmoniem: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Mona Jaber: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Ali Anwar: Department of Computer Science and Engineering, University of Minnesota Twin-Cities, Minneapolis, MN 55455, USA
Yuchao Zhang: School of Computer Science, Beijing University of Posts and Telecommunication, Beijing 100876, China
Mingliang Gao: School of Electrical and Electronic Engineering, Shandong University of Technology, Qingdao 255049, China

Future Internet, 2024, vol. 16, issue 11, 1-13

Abstract: Nowadays, Edge Intelligence has seen unprecedented growth in most of our daily life applications. Traditionally, most applications required significant efforts into data collection for data-driven analytics, raising privacy concerns. The proliferation of specialized hardware on sensors, wearable, mobile, and IoT devices has led to the growth of Edge Intelligence, which has become an integral part of the development cycle of most modern applications. However, scalability issues hinder their wide-scale adoption. We aim to focus on these challenges and propose a scalable decentralized edge intelligence framework. Therefore, we analyze and empirically evaluate the challenges of existing methods, and design an architecture that overcomes these challenges. The proposed approach is client-driven and model-centric, allowing models to be shared between entities in a scalable fashion. We conduct experiments over various benchmarks to show that the proposed approach presents an efficient alternative to the existing baseline method, and it can be a viable solution to scale edge intelligence.

Keywords: collaborative computing; distributed systems; data-driven intelligence; machine learning; model distillation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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