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.

翻译：通过将待请求内容存储于无线视频缓存网络的多个层级，可缓解少数热门文件视频流量造成的回程网络拥塞。通常，内容服务提供商拥有内容所有权，用户通过其（无线）互联网服务提供商向内容服务提供商请求偏好内容。由于各方均不公开其私有信息与商业机密，传统技术难以有效预测用户未来需求的动态变化。为此，我们提出一种新颖的资源感知分层联邦学习解决方案，用于预测用户未来的内容请求。该方案采用实用的数据采集技术，允许用户根据请求内容更新本地训练数据集。此外，鉴于网络与其他计算资源有限，考虑到仅部分用户参与模型训练，我们推导了所提算法的收敛界。基于此界限，通过最小化加权效用函数来联合配置可控参数，从而在实际资源约束下高效训练资源感知分层联邦学习模型。大量仿真结果验证了所提算法在测试精度与能耗成本方面均优于现有基线方法。