Large artificial intelligence (AI) models exhibit remarkable capabilities in various application scenarios, but deploying them at the network edge poses significant challenges due to issues such as data privacy, computational resources, and latency. In this paper, we explore federated fine-tuning and collaborative reasoning techniques to facilitate the implementation of large AI models in resource-constrained wireless networks. Firstly, promising applications of large AI models within specific domains are discussed. Subsequently, federated fine-tuning methods are proposed to adapt large AI models to specific tasks or environments at the network edge, effectively addressing the challenges associated with communication overhead and enhancing communication efficiency. These methodologies follow clustered, hierarchical, and asynchronous paradigms to effectively tackle privacy issues and eliminate data silos. Furthermore, to enhance operational efficiency and reduce latency, efficient frameworks for model collaborative reasoning are developed, which include decentralized horizontal collaboration, cloud-edge-end vertical collaboration, and multi-access collaboration. Next, simulation results demonstrate the effectiveness of our proposed methods in reducing the fine-tuning loss of large AI models across various downstream tasks. Finally, several open challenges and research opportunities are outlined.

翻译：大型人工智能模型在多种应用场景中展现出卓越能力，但在网络边缘部署这些模型面临着数据隐私、计算资源和延迟等重大挑战。本文探讨了联邦微调与协同推理技术，以促进大型AI模型在资源受限的无线网络中的实施。首先，讨论了大型AI模型在特定领域内的应用前景。随后，提出了联邦微调方法，使大型AI模型能够适应网络边缘的特定任务或环境，有效应对通信开销相关的挑战并提升通信效率。这些方法遵循集群化、分层化和异步化范式，以有效解决隐私问题并消除数据孤岛。此外，为提升运行效率并降低延迟，开发了模型协同推理的高效框架，包括去中心化水平协同、云-边-端垂直协同以及多接入协同。接下来，仿真结果证明了我们提出的方法在降低大型AI模型跨多种下游任务微调损失方面的有效性。最后，概述了若干开放挑战与研究机遇。