  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.
