Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research.

翻译：在人工智能（AI）与大数据的强劲推动下，边缘智能（EI）作为一种新兴计算范式应运而生，它将AI与边缘计算（EC）相结合，成为释放AI服务全部潜能的典范解决方案。然而，通信成本、资源分配、隐私和安全等方面的挑战仍制约着其在支持多样化需求服务方面的效能。针对这些问题，本文引入社交化学习（SL）作为一种前景广阔的解决方案，以进一步推动EI的发展。SL是一种基于社会原则与行为的学习范式，旨在增强EI系统内智能体的协作能力与集体智慧。SL不仅提升了系统的适应性，还优化了通信与组网过程，这对于跨异构设备与平台的分布式智能至关重要。因此，SL与EI的结合有望极大促进未来网络中协同智能的发展。本文呈现了关于EI与SL融合的文献综述结果，总结了现有EI与SL研究的最新成果。随后，我们全面探讨了EI的局限性以及SL可能为其带来的益处，特别聚焦于这些系统中的通信挑战、组网策略及其他方面，强调了优化网络解决方案对提升系统效率的作用。基于这些讨论，我们详细阐述了社交化架构、社交化训练与社交化推理这三个集成组件，并分析了其优缺点。最后，我们指出了SL与EI结合可能的一些未来应用方向，讨论了开放性问题并提出了若干未来研究建议。