Video caching can significantly improve backhaul traffic congestion by locally storing the popular content that users frequently request. A privacy-preserving method is desirable to learn how users' demands change over time. As such, this paper proposes a novel resource-aware hierarchical federated learning (RawHFL) solution to predict users' future content requests under the realistic assumptions that content requests are sporadic and users' datasets can only be updated based on the requested content's information. Considering a partial client participation case, we first derive the upper bound of the global gradient norm that depends on the clients' local training rounds and the successful reception of their accumulated gradients over the wireless links. Under delay, energy and radio resource constraints, we then optimize client selection and their local rounds and central processing unit (CPU) frequencies to minimize a weighted utility function that facilitates RawHFL's convergence in an energy-efficient way. Our simulation results show that the proposed solution significantly outperforms the considered baselines in terms of prediction accuracy and total energy expenditure.

翻译：视频缓存通过本地存储用户频繁请求的热门内容，能显著改善回程网络流量拥塞问题。为掌握用户需求随时间的变化规律，亟需一种隐私保护型学习方法。基于此，本文提出一种新颖的资源感知分层联邦学习（RawHFL）解决方案，在内容请求呈零星分布且用户数据集仅能基于所请求内容信息进行更新的现实假设下，预测用户未来的内容请求。针对部分客户端参与的场景，本文首先推导了全局梯度范数的上界，该上界取决于客户端的本地训练轮次及其累积梯度在无线链路上的成功接收情况。在时延、能量和无线资源约束条件下，本文进一步优化客户端选择、本地训练轮次及中央处理器（CPU）频率，以最小化促进RawHFL能效型收敛的加权效用函数。仿真结果表明，所提方案在预测精度和总能耗方面均显著优于现有基准方案。