6G网络中普适人工智能即服务的零接触实现
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
AI总结：
针对6G网络中普适AI服务落地的需求，提出基于区块链智能系统的零接触PAIaaS平台架构，通过标准化AI服务与统一接口实现跨域部署，并通过联邦学习即服务用例验证了其自优化、自适应及降本的能力。
摘要：
即将到来的6G技术以超密集网络、低时延、高速数据传输为特征，其愿景是通过支持自X（例如自配置、自监控、自愈）服务的零接触方案来支撑普适AI（Pervasive AI，PAI）。然而，6G研究仍处于起步阶段，目前仅在概念化设计、探究实现方式及规划用例方面迈出了初步步伐。为此，学术界和产业界已逐渐从AI分布式部署的理论研究转向实际落地与标准化工作。但目前尚未充分探索如何设计一个端到端框架，通过零接触服务供应辅助的第三方应用简化服务访问流程，从而实现AI分布式部署的体系化。在此背景下，我们提出一种新型平台架构，用于在6G网络中部署零接触的普适AI即服务（PAIaaS），该架构由基于区块链的智能系统提供支持。该平台旨在在架构的所有层级实现普适AI的标准化，并统一接口，以推动服务在应用域和基础设施域的跨域部署，缓解用户在成本、安全、资源分配方面的顾虑，同时满足6G的严苛性能要求。作为概念验证，我们展示了一个联邦学习即服务的用例，在该用例中我们评估了所提系统的自优化能力、对6G网络动态的自适应能力，以及降低用户感知成本的效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
