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

TL;DR

This survey reviews the characteristics, challenges, and solutions for establishing trustworthiness in Edge Intelligence, emphasizing security, reliability, transparency, and sustainability in resource-constrained, heterogeneous environments.

Contribution

It provides a comprehensive overview of trustworthy Edge Intelligence, including definitions, architecture, enabling technologies, and a review of state-of-the-art solutions and open challenges.

Findings

Identifies key characteristics and challenges of trustworthy EI.

Summarizes enabling technologies and solutions for trustworthiness.

Highlights open issues and future research directions.

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.