Backhaul traffic congestion caused by the video traffic of a few popular files can be alleviated by storing the to-be-requested content at various levels in wireless video caching networks. Typically, content service providers (CSPs) own the content, and the users request their preferred content from the CSPs using their (wireless) internet service providers (ISPs). As these parties do not reveal their private information and business secrets, traditional techniques may not be readily used to predict the dynamic changes in users' future demands. Motivated by this, we propose a novel resource-aware hierarchical federated learning (RawHFL) solution for predicting user's future content requests. A practical data acquisition technique is used that allows the user to update its local training dataset based on its requested content. Besides, since networking and other computational resources are limited, considering that only a subset of the users participate in the model training, we derive the convergence bound of the proposed algorithm. Based on this bound, we minimize a weighted utility function for jointly configuring the controllable parameters to train the RawHFL energy efficiently under practical resource constraints. Our extensive simulation results validate the proposed algorithm's superiority, in terms of test accuracy and energy cost, over existing baselines.

通过在无线视频缓存网络中的不同级别上存储待请求内容，可以缓解因少数热门文件的视频流量导致的回传链路拥塞问题。为了预测用户未来的内容请求，我们提出了一种新颖的资源感知的分层联邦学习（RawHFL）解决方案，使用实践中的资源限制以高效地训练RawHFL能源。通过权重实用函数的最小化来联合配置可控参数，我们的算法在测试准确性和能量成本方面优于现有基线模型。

无线视频缓存网络中的资源感知分层联邦学习