剖析边缘AI安全与可靠性挑战，呼吁系统性研究
SoK: Towards Security and Safety of Edge AI
- 梳理边缘AI面临的安全与可靠性威胁
- 总结现有防护措施并指出关键短板
- 适合关注AI落地安全的研究者与工程师
先进AI应用正广泛普及，如集中式大语言模型（LLMs）。但这种集中化既带来风险，也形成性能瓶颈。边缘AI（Edge AI）有望解决这些问题，其去中心化特性却引入了新的安全与可靠性挑战。本文强调安全与可靠性对边缘AI至关重要，且二者需协同考虑。我们系统调研了相关威胁，总结现有应对措施，并归纳开放问题，呼吁更多研究投入。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。