To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independently

翻译：为实现人工智能的大规模高效部署，人工智能与边缘计算的结合催生了边缘智能技术，该技术利用终端设备和边缘服务器的计算与通信能力，在数据生成源头附近进行数据处理。边缘智能的关键技术之一是联邦学习，这是一种保护隐私的机器学习范式，使数据拥有者无需将原始数据传输至第三方服务器即可训练模型。然而，联邦学习网络需涉及数千个异构分布式设备，导致通信效率成为核心瓶颈。为减少节点故障与设备退出，本文提出一种分层联邦学习框架，通过指定集群领导者执行中间模型聚合以支持数据拥有者。因此，在提升边缘服务器资源利用率的基础上，本文可有效弥补缓存容量限制。为缓解软点击对用户体验质量的影响，作者将用户体验质量建模为综合系统成本。针对该公式化问题，作者提出一种基于联邦深度强化学习与联邦学习的去中心化缓存算法，使多个智能体能够独立学习与决策。