# Roadmap for Edge AI: A Dagstuhl Perspective

arXiv: https://arxiv.org/abs/2112.00616 · HTML: https://mlsanity.com/paper/2112.00616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [6G White Paper on Edge Intelligence](https://mlsanity.com/paper/2004.14850/) (2020)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?](https://mlsanity.com/paper/1812.03557/) (2018)
- [Computational Models of Human Decision-Making with Application to the Internet of Everything](https://mlsanity.com/paper/2008.11958/) (2020)
- [Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence](https://mlsanity.com/paper/2012.00419/) (2020)
- [Revisiting the Arguments for Edge Computing Research](https://mlsanity.com/paper/2106.12224/) (2021)

## Built on by (most cited first, 4 of 4)

- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Future of Edge AI in biodiversity monitoring](https://mlsanity.com/paper/2602.13496/) (2026)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [A Path to Holistic Privacy in Stream Processing Systems](https://mlsanity.com/paper/2305.11638/) (2023)

## Abstract (arXiv)

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.