Editorials in Scientific Journals:
A. Ding, E. Peltonen, T. Meuser, A. Aral, C. Becker, S. Dustdar, T. Hiessl, D. Kranzlmüller, M. Liyanage, S. Maghsudi, N. Mohan, J. Ott, J. Rellermeyer, S. Schulte, H. Schulzrinne, G. Solmaz, S. Tarkoma, B. Varghese, L. Wolf:
"Roadmap for Edge AI: A Dagstuhl Perspective";
ACM SIGCOMM Computer Communication Review,
Volume 52
(2022),
Issue 1;
28
- 33.
English 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, optimisation, 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.
Keywords:
Edge AI, Edge Computing, 5G Beyond, Future Cloud, Roadmap
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3523230.3523235
Created from the Publication Database of the Vienna University of Technology.