Roadmap for Edge AI: A Dagstuhl Perspective

TL;DR

This paper provides a comprehensive roadmap for Edge AI, emphasizing its role in enabling adaptive, secure, and efficient data-driven applications through novel ML methods and collaborative efforts.

Contribution

It offers a collective vision and strategic roadmap for advancing Edge AI research, development, and collaboration based on expert insights from the Dagstuhl Seminar.

Findings

Edge AI enhances data-driven application adaptation.

It supports secure, trustworthy, and privacy-preserving AI deployment.

The paper outlines a collaborative roadmap for future Edge AI advancements.

Abstract

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.