Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then {a popular} content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.

翻译：边缘缓存通过赋能小蜂窝基站（SBS）中的缓存单元，使用户设备（UE）能够获取已预存在SBS中的请求内容，是下一代网络中极具前景的解决方案。SBS在保护用户个人信息的同时，通过机器学习准确预测流行内容至关重要。传统联邦学习（FL）虽能保护用户隐私，但UE间的数据差异会导致模型质量下降。因此，需要为每个UE训练个性化本地模型以准确预测流行内容。此外，下一代网络中相邻SBS可共享缓存内容，因此不同SBS中缓存的预测流行内容将影响内容获取成本。故而，协作确定流行内容的缓存位置成为关键。针对上述问题，本文提出一种基于弹性联邦与多智能体深度强化学习的协作边缘缓存方案（CEFMR），以优化网络成本。我们首先设计弹性联邦学习算法为每个UE训练个性化模型，采用对抗自编码器（AAE）模型提升预测精度，并基于训练完成的AAE模型提出流行内容预测算法。最后提出基于多智能体深度强化学习（MADRL）的算法，协同决策预测流行内容在各SBS间的缓存位置。实验结果表明，本方案相较于现有基线缓存方案具有显著优越性。