Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a
central, native element. When 6G systems are deployed at a large scale,
end-to-end AI-based solutions will necessarily have to encompass both the radio
and the fiber-optical domain. This paper introduces the Decentralized
Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G
networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse
network platforms and thus facilitates networks that autonomously configure,
monitor, and repair themselves. This is particularly crucial at the network
edge, where advanced applications meet heightened functionality and security
demands. The radio/optical integration is vital due to the current
compartmentalization of AI research within these domains, which lacks a
comprehensive understanding of their interaction. Our approach explores
multi-network orchestration and AI control integration, filling a critical gap
in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI
framework is a step towards a global standard for AI in 6G, aiming to establish
reference use cases, data and model management methods, and benchmarking
platforms for future AI/ML solutions.

在部署大规模的 6G 系统时，人工智能被视为中心和本地元素。本文介绍了用于将人工智能整合到规模化的 6G 网络中的分散式多方多网络人工智能（DMMAI）框架。该框架通过协调跨不同网络平台的基于人工智能的控制，实现了网络的自动配置、监控和自我修复。我们的方法探索了多网络编排和人工智能控制的集成，填补了 6G 网络中以人工智能驱动的协调标准化框架的重要空白。DMMAI 框架是实现 6G 中人工智能全球标准的一步，旨在建立参考用例、数据和模型管理方法以及未来人工智能 / 机器学习解决方案的基准平台。