Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

• URL: http://arxiv.org/abs/2404.13348v4
• Date: Sun, 03 Nov 2024 06:45:47 GMT
• Title: Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
• Authors: Xiaofei Wang, Yunfeng Zhao, Chao Qiu, Qinghua Hu, Victor C. M. Leung,
• Abstract summary: This paper presents the findings of a literature review on the integration of edge intelligence (EI) and socialized learning (SL) SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents. We elaborate on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses.
• Score: 62.252355444948904
• License:
• Abstract: 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.

Related papers

• Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges [18.98619510865057]
  Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by leveraging the computational capabilities of edge networks. The integration of Large Language Models (LLMs) empowers EI to evolve into the next stage: Edge General Intelligence (EGI) This survey delineates the distinctions between EGI and traditional EI, categorizing LLM-empowered EGI into three conceptual systems: centralized, hybrid, and decentralized.
  arXiv  Detail & Related papers  (2024-10-16T07:45:31Z)
• A Survey on Integrated Sensing, Communication, and Computation [57.6762830152638]
  The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then explores the state-of-the-art signal designs for
  arXiv  Detail & Related papers  (2024-08-15T11:01:35Z)
• Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• Operating System And Artificial Intelligence: A Systematic Review [17.256378758253437]
  We explore how AI-driven tools enhance OS performance, security, and efficiency, while OS advancements facilitate more sophisticated AI applications. We analyze various AI techniques employed to optimize OS functionalities, including memory management, process scheduling, and intrusion detection. We explore the promising prospects of Intelligent OSes, considering not only how innovative OS architectures will pave the way for groundbreaking opportunities but also how AI will significantly contribute to advancing these next-generation OSs.
  arXiv  Detail & Related papers  (2024-07-19T05:29:34Z)
• Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions [47.791246017237]
  Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies. This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs.
  arXiv  Detail & Related papers  (2024-07-05T15:23:43Z)
• Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future [59.78608958395464]
  We build a Social AI Data Infrastructure, which consists of a comprehensive social AI taxonomy and a data library of 480 NLP datasets. Our infrastructure allows us to analyze existing dataset efforts, and also evaluate language models' performance in different social intelligence aspects. We show there is a need for multifaceted datasets, increased diversity in language and culture, more long-tailed social situations, and more interactive data in future social intelligence data efforts.
  arXiv  Detail & Related papers  (2024-02-28T00:22:42Z)
• Towards Integrated Fine-tuning and Inference when Generative AI meets Edge Intelligence [5.078859563367533]
  High-performance generative artificial intelligence (GAI) represents latest evolution of computational intelligence. The inevitable encounter between GAI and edge intelligence (EI) can unleash new opportunities. We propose the GAI-oriented synthetical network (GaisNet) that buffers contradiction leveraging data-free knowledge relay.
  arXiv  Detail & Related papers  (2024-01-05T06:52:55Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems [6.699060157800401]
  Internet of Vehicles (IoV) is transformed into an Intelligent Transportation System (ITS) To address these challenges, Federated Learning, a collaborative and distributed intelligence technique, is suggested. With a multitude of use cases and benefits, Federated Learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.
  arXiv  Detail & Related papers  (2021-02-19T20:07:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.