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arXiv · 2310.17944

A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability

Xiaojie Wang·Beibei Wang·Yu Wu·Zhaolong Ning·Song Guo·Fei Richard Yu
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Abstract

Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we summarize several important issues that hinder the achievement of trustworthy EI. Subsequently, we present enabling technologies for trustworthy EI systems and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues.

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Xiaojie Wang, Beibei Wang, Yu Wu, Zhaolong Ning, Song Guo, Fei Richard Yu. 2023-10-27. A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability. https://arxiv.org/abs/2310.17944

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