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：回程链路拥塞问题，通常由少数热门文件的视频流量引起，可通过在无线视频缓存网络的不同层级存储待请求内容来缓解。通常，内容服务提供商（CSP）拥有内容所有权，用户通过（无线）互联网服务提供商（ISP）向CSP请求其偏好的内容。由于这些双方不愿泄露各自隐私信息和商业机密，传统技术可能难以直接用于预测用户未来需求的动态变化。受此启发，我们提出了一种新颖的资源感知分层联邦学习（RawHFL）解决方案，用于预测用户未来的内容请求。该方案采用实用数据采集技术，允许用户根据其请求内容更新本地训练数据集。此外，考虑到网络及计算资源有限，且仅有部分用户参与模型训练，我们推导了所提算法的收敛界。基于该收敛界，我们最小化一个加权效用函数，以联合配置可控参数，在现实资源约束下高效训练RawHFL。广泛的仿真结果验证了所提算法在测试精度和能耗方面相较于现有基准方法的优越性。