Advanced AI applications have become increasingly available to a broad audience, e.g., as centrally managed large language models (LLMs). Such centralization is both a risk and a performance bottleneck - Edge AI promises to be a solution to these problems. However, its decentralized approach raises additional challenges regarding security and safety. In this paper, we argue that both of these aspects are critical for Edge AI, and even more so, their integration. Concretely, we survey security and safety threats, summarize existing countermeasures, and collect open challenges as a call for more research in this area.
@article{arxiv.2410.05349,
title = {SoK: Towards Security and Safety of Edge AI},
author = {Tatjana Wingarz and Anne Lauscher and Janick Edinger and Dominik Kaaser and Stefan Schulte and Mathias Fischer},
journal= {arXiv preprint arXiv:2410.05349},
year = {2024}
}