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 efficacy. 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）和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，将AI与边缘计算（EC）相结合，成为释放AI服务全部潜力的典范解决方案。然而，通信成本、资源分配、隐私和安全方面的挑战仍制约其支持多样化需求服务的能力。针对这些问题，本文提出社会化学习（SL）作为一种有前景的解决方案，进一步推动EI的发展。SL是一种基于社会原则和行为的学习范式，旨在增强EI系统中智能体的协作能力和集体智慧。SL不仅提升了系统的适应性，还优化了通信和网络过程，这对跨多种设备和平台的分布式智能至关重要。因此，SL与EI的结合将极大促进未来网络中协作智能的发展。本文综述了EI与SL融合的相关文献，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的局限性及其如何从SL中受益，特别强调了这些系统中的通信挑战、网络策略及其他方面，突出了优化网络解决方案在提升系统效能中的作用。基于这些讨论，我们详细阐述了三个整合组件：社会化架构、社会化训练和社会化推理，分析了它们的优缺点。最后，我们指出了结合SL与EI的一些未来可能应用，讨论了开放性问题，并提出了未来研究方向。