Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
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Abstract (arXiv)
The vision of the upcoming 6G technologies, characterized by ultra-dense network, low latency, and fast data rate is to support Pervasive AI (PAI) using zero-touch solutions enabling self-X (e.g., self-configuration, self-monitoring, and self-healing) services. However, the research on 6G is still in its infancy, and only the first steps have been taken to conceptualize its design, investigate its implementation, and plan for use cases. Toward this end, academia and industry communities have gradually shifted from theoretical studies of AI distribution to real-world deployment and standardization. Still, designing an end-to-end framework that systematizes the AI distribution by allowing easier access to the service using a third-party application assisted by a zero-touch service provisioning has not been well explored. In this context, we introduce a novel platform architecture to deploy a zero-touch PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart system. This platform aims to standardize the pervasive AI at all levels of the architecture and unify the interfaces in order to facilitate the service deployment across application and infrastructure domains, relieve the users worries about cost, security, and resource allocation, and at the same time, respect the 6G stringent performance requirements. As a proof of concept, we present a Federated Learning-as-a-service use case where we evaluate the ability of our proposed system to self-optimize and self-adapt to the dynamics of 6G networks in addition to minimizing the users' perceived costs.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 21 Jul 2023.
Builds on
4 of 14 references.
References · 4
- 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
- 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
- 2020FLaaS: Federated Learning as a Servicecited by 22
- 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
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Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 1.
7 papers.
- 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
- 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
- 2024A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Frameworkcited by 3
- 2023AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Methodcited by 1
- 2025Closing the Responsibility Gap in AI-based Network Management: An Intelligent Audit System Approachcited by 1
- 2023AI-based Self-healing Solutions Applied to Cellular Networks: An Overviewcited by 0
- 2026Hybrid Responsible AI-Stochastic Approach for SLA Compliance in Multivendor 6G Networkscited by 0
- 2026Hybrid Responsible AI-Stochastic Approach for SLA Compliance in Multivendor 6G Networks10 Feb 2026
- 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey3 May 2025
- 2025Closing the Responsibility Gap in AI-based Network Management: An Intelligent Audit System Approach8 Feb 2025
- 2024A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework7 Aug 2024
- 2023AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method16 Nov 2023
- 2023AI-based Self-healing Solutions Applied to Cellular Networks: An Overview4 Nov 2023
- 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications8 Oct 2023