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，可显著改善回程网络流量拥塞。为实现隐私保护下用户需求随时间动态变化的学习，本文提出一种新颖的资源感知层次化联邦学习（RawHFL）方案，在内容请求具有稀疏性且用户数据集仅能基于所请求内容信息更新的现实假设下，预测用户未来的内容需求。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概率。进一步，在时延、能量和无线资源约束下，优化客户端的选取策略、本地更新轮次及中央处理器（CPU）频率，以最小化促进RawHFL能效收敛的加权效用函数。仿真结果表明，所提方案在预测精度与总能耗方面均显著优于对比基准方法。