mlsanityData to 7 Oct 2026

Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks

arXiv 2307.11468 ↗First posted 21 Jul 2023cs.AI cs.DC cs.NIcited by 7

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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

  1. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  2. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
  3. 2020FLaaS: Federated Learning as a Servicecited by 22
  4. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 1.

7 papers.