The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.

边缘机器学习与可信边缘机器学习（EML）的融合是一个备受关注的研究领域，通过利用分布式网络资源以合作方式进行联合训练和推理。然而，EML面临着多种挑战，包括资源限制、异构网络环境和不同应用的多样化服务要求，这些都影响了EML在利益相关者眼中的可信度。本调研综述对可信EML的定义、属性、框架、技术和解决方案进行了综合总结。特别是，我们首先强调了在第六代（6G）网络背景下可信EML的重要性。然后，我们从部署和实际应用场景的角度讨论了可信度的必要性。接下来，我们给出了对可信EML的初步定义，并探讨了它的关键属性。随后，我们介绍了可信EML系统的基本框架和支持技术，并对增强EML可信度的最新解决方案进行了深入文献综述。最后，我们讨论了相关的研究挑战和未解决问题。

值得信赖的边缘机器学习：一项调查