Roadmap for Edge AI: A Dagstuhl Perspective

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.

for ACM SIGCOMM CCR

References in corpus (3)

• A Survey on Distributed Machine Learning
• Computational Models of Human Decision-Making with Application to the Internet of Everything
• Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?

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• A Survey on Privacy for B5G/6G: New Privacy Challenges, and Research Directions
• Complexity-Driven CNN Compression for Resource-constrained Edge AI
• A Path to Holistic Privacy in Stream Processing Systems