## 3 Key Enabling Technologies for AI-Driven 6G Networks
### Cross-Domain AI Collaboration  
AI capabilities in 6G networks will be deployed in both distributed and centralized ways. AI on the RAN side may be distributed across the CU and DU, while, on the CN side, NWDAFs will be deployed in a distributed manner. Data centers and the cloud will also provide centralized computing and AI services, such as large language models. Therefore, cross-domain AI collaboration will be a natural requirement for AI-driven 6G. This collaboration will be reflected not only in the cooperation between networks and terminals, but also in the cooperation between physical domains and digital twin domains. Figure 4 illustrates the collaboration of AI across multiple domains, where each domain will provide AI control and management within its own domain and the network management system will offer end-to-end AI orchestration domain management to match the requirements and computing resources of different AI tasks from a global perspective.  
The distributed deployment of AI across different domains may cause numerous challenges for 6G technology, especially in terms of data security and privacy issues. Distributed learning has emerged as a critical technique for incorporating AI algorithms into 6G network systems which can process and analyze substantial data across geographically distributed environments through edge computing. In the typical distributed learning paradigm, the training and inference tasks are distributed across multiple computational nodes, which may range from edge devices to cloud servers. The advantage of distributed learning lies in its effectiveness in harnessing the computational power of multiple devices while preserving data privacy.  
Furthermore, with the introduction of generative AI technologies, 6G networks have a stronger demand for collaboration between models. Large models require massive numbers of data and computing resources to support the training of large-scale parameter models, typically deployed in central nodes or the cloud. However, large models generally offer general AI capabilities, such as text interaction and semantic understanding. When addressing specific scenarios within different domains of the network, general large models cannot independently meet the AI requirements, and collaboration with domain-specific smaller models is inevitable. Therefore, the coordination between large models and small models has become a research hotspot. By deploying the large models in the cloud and the small models on the edge, a hybrid edge-cloud collaborative model inference architecture is proposed [24]. The inference results of small models, denoted as the probability of draft tokens, are compared with the results of large models, which can give a quick response on whether to accept or reject the draft tokens. If the draft token is rejected, resampling is executed by large models to generate the final output [25]. It showcases a promising and feasible solution that enables telecom operators to deploy large models for mobile devices which can take full advantage of the high inference capability of large models while leveraging the distributed computing resources on the devices.