Journal Article10.1109/OJCOMS.2023.3265425
Distributed Intelligence in Wireless Networks
Xiaolan Liu,Yuanwei Liu,Yue-lin Gao,Toktam Mahmoodi,Sangarapillai Lambotharan,Danny H. K. Tsang +5 more
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TL;DR: A comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks is conducted, with a focus on the basic concepts ofnative- AI wireless networks, on the AIenabled edge computing, and the design of distributed learning architectures for heterogeneous networks.
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Abstract: The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security and privacy concerns caused by billions of connected wireless devices and typically zillions of bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of the large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Besides, the deployment of AI techniques to redesign end-to-end communication is attracting attention to improve communication performance. Therefore, the interaction of AI and wireless communications generates a new concept, named native AI wireless networks. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native AI wireless networks, with a focus on the design of distributed learning architectures for heterogeneous networks, on AI-enabled edge computing, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to state-of-the-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify potential future opportunities.
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Citations
Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges
Abdulraqeb Alhammadi,Ibraheem Shayea,Ayman A. El‐Saleh,Marwan Hadri Azmi,Zool Hilmi Ismail,Lida Kouhalvandi,Sawsan Ali Saad +6 more
TL;DR: AI-enabled wireless networks offer improved performance, automation, data analysis, insights, and learning capabilities. AI technologies have the potential to revolutionize wireless networks and enable new applications. However, challenges remain in unsolved research areas. This paper explores AI-enabled wireless networks, their applications, and challenges.
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Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey
Mai Le,Thien Huynh-The,Tan Do-Duy,Thai-Hoc Vu,Won-Joo Hwang,Quoc-Viet Pham +5 more
TL;DR: This work provides a background of machine learning and a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference, and provides an extensive review of distributed learning for critical IoT services and applications.
Exploring Personalized Internet of Things (PIoT), social connectivity, and Artificial Social Intelligence (ASI): A survey
TL;DR: This survey explores Personalized Internet of Things (PIoT), Social Internet of Things (SIoT), and Artificial Social Intelligence (ASI), highlighting their evolution, challenges, and applications, including a case study on PIoT in post-COVID scenarios, and provides a comprehensive review of the field.
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Distributing Intelligence for 6G Network Automation: Performance and Architectural Impact
Sayantini Majumdar,Riccardo Trivisonno,Wint Yi Poe,Georg Carle +3 more
- 28 May 2023
TL;DR: This work examines the impact of distributed AI, by analyzing its performance and how the existing 5G architecture could be enhanced to support it in 6G, by selecting a relevant beyond 5G use case - auto-scaling virtual resources in a network slice.
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How Can AI be Distributed in the Computing Continuum? Introducing the Neural Pub/Sub Paradigm
Lauri Lovén,Roberto Morabito,Abhishek Kumar,Susanna Pirttikangas,Jukka Riekki,Sasu Tarkoma +5 more
TL;DR: The neural publish/subscribe paradigm is proposed, a novel approach to orchestrating AI workflows in large-scale distributed AI systems in the computing continuum that aims to overcome limitations by efficiently managing training, fine-tuning and inference workflows, improving distributed computation, facilitating dynamic resource allocation, and enhancing system resilience across the Computing continuum.
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