  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
