SoK: Towards Security and Safety of Edge AI

Authors: Tatjana Wingarz, Anne Lauscher, Janick Edinger, Dominik Kaaser, Stefan Schulte, Mathias Fischer
Published: 2024-10-07

Source: https://arxiv.org/abs/2410.05349

PDF: https://arxiv.org/pdf/2410.05349

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プライバシー保護 バイアス 防御手法

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Abstract

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.