The rise of big data and AI boosts the development of future wireless networks. However, due to the high cost of data offloading and model training, it is challenging to implement network intelligence based on the existing centralized learning strategies, especially at the edge of networks. To provide a feasible solution, a paradigm of federated learning- enabled intelligent F-RANs is proposed, which can take full advantage of fog computing and AI. The fundamental theory with respect to the accuracy loss correction and the model compression is studied, which can provide some insights into the design of federated learning in F-RANs. To support the implementation of federated learning, some key techniques are introduced to fully integrate the communication, computation, and storage capability of F-RANs. Moreover, future trends of federated learning-enabled intelligent F-RANs, such as potential applications and open issues, are discussed.

中文翻译：

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支持联合学习的智能雾无线接入网：基本理论，关键技术和未来趋势

大数据和人工智能的兴起推动了未来无线网络的发展。然而，由于数据卸载和模型训练的高昂成本，基于现有的集中式学习策略（尤其是在网络边缘）实现网络智能是一项挑战。为了提供可行的解决方案，提出了一种联邦学习使能的智能F-RAN范例，该范例可以充分利用雾计算和AI。研究了有关精度损失校正和模型压缩的基础理论，可以为F-RAN中的联合学习设计提供一些见识。为了支持联合学习的实施，引入了一些关键技术以完全集成F-RAN的通信，计算和存储功能。此外，