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

TL;DR

This paper proposes a novel blockchain-supported platform architecture for zero-touch Pervasive AI-as-a-Service in 6G networks, enabling automated, secure, and efficient AI deployment and management.

Contribution

It introduces a new platform architecture for zero-touch PAIaaS in 6G, supported by blockchain, to standardize AI services and facilitate deployment across domains.

Findings

The platform can self-optimize and adapt to 6G network dynamics.

It reduces user-perceived costs and enhances security.

Demonstrated through a Federated Learning-as-a-Service use case.

Abstract

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.