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.
- Setareh Maghsudi and Max Davy. 2021. Computational Models of Human Decision-Making with Application to the Internet of Everything. IEEE Wireless Communications 28, 1 (2021), 152–159.Google ScholarCross Ref
- Setareh Maghsudi and Mihaela van der Schaar. 2019. Distributed Task Management in Cyber-Physical Systems: How to Cooperate Under Uncertainty? IEEE Trans. on Cognitive Communications and Networking 5, 1 (2019), 165–180.Google ScholarCross Ref
- Marja Matinmikko-Blue, Sirpa Aalto, Muhammad Imran Asghar, Hendrik Berndt, Yan Chen, Sudhir Dixit, Risto Jurva, Pasi Karppinen, Markku Kekkonen, Marianne Kinnula, et al. 2020. White paper on 6G drivers and the UN SDGs. 6G RESEARCH VISIONS, NO. 2 (2020).Google Scholar
- Nitinder Mohan, Lorenzo Corneo, Aleksandr Zavodovski, Suzan Bayhan, Walter Wong, and Jussi Kangasharju. 2020. Pruning Edge Research with Latency Shears. In Proceedings of the 19th ACM Workshop on Hot Topics in Networks (HotNets '20). 182–189. Google ScholarDigital Library
- Ella Peltonen and et al. 2020. 6G white paper on edge intelligence. 6G RESEARCH VISIONS, NO. 8 (2020).Google Scholar
- Md. Lushanur Rahman, J. Andrew Zhang, Kai Wu, Xiaojing Huang, Y. Jay Guo, Shanzhi Chen, and Jinhong Yuan. 2020. Enabling Joint Communication and Radio Sensing in Mobile Networks - A Survey. ArXiv abs/2006.07559 (2020).Google Scholar
- Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu. 2020. Data poisoning attacks against federated learning systems. In European Symposium on Research in Computer Security. Springer, 480–501.Google ScholarDigital Library
- Wiebke Toussaint and Aaron Yi Ding. 2020. Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence. In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI). 177–184.Google Scholar
- Wiebke Toussaint, Akhil Mathur, Aaron Yi Ding, and Fahim Kawsar. 2021. Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys '21). ACM, 439–445. Google ScholarDigital Library
- Blesson Varghese, Eyal de Lara, Aaron Yi Ding, Cheol-Ho Hong, Flavio Bonomi, Schahram Dustdar, Paul Harvey, Peter Hewkin, Weisong Shi, Mark Thiele, and Peter Willis. 2021. Revisiting the Arguments for Edge Computing Research. IEEE Internet Computing 25, 5 (2021), 36–42. Google ScholarCross Ref
- Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, and Jan S. Rellermeyer. 2020. A Survey on Distributed Machine Learning. ACM Comput. Surv. 53, 2, Article 30 (March 2020), 33 pages. Google ScholarDigital Library
- Q. Ye and Y. Zhang. 2016. Participation Behavior and Social Welfare in Repeated Task Allocations. In 2016 IEEE International Conference on Agents (ICA). 94–97.Google Scholar
Index Terms
- Roadmap for edge AI: a Dagstuhl perspective
Recommendations
Edge Intelligence: Concepts, Architectures, Applications, and Future Directions
The name edge intelligence, also known as Edge AI, is a recent term used in the past few years to refer to the confluence of machine learning, or broadly speaking artificial intelligence, with edge computing. In this article, we revise the concepts ...
Towards Named AI Networking: Unveiling the Potential of NDN for Edge AI
Ad-Hoc, Mobile, and Wireless NetworksAbstractThanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the ...
Pyramid: Enabling Hierarchical Neural Networks with Edge Computing
WWW '22: Proceedings of the ACM Web Conference 2022Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model training and inference at the network edge on edge servers. Compared ...
Comments