Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solution for a specific local edge cache is to collect more request histories from other edge caches. However, uniformly merging these request histories may not perform satisfactorily due to heterogeneous content distributions on different edges. To solve this problem, we propose a collaborative edge caching framework. First, we design a meta-learning-based collaborative strategy to guarantee that the local model can timely meet the continually changing content popularity. Then, we design an edge sampling method to select more "valuable" neighbor edges to participate in the local training. To evaluate the proposed framework, we conduct trace-driven experiments to demonstrate the effectiveness of our design: it improves the average cache hit rate by up to $10.12\%$ (normalized) compared with other baselines.

翻译：当前基于学习的边缘缓存方案通常面临动态内容流行度的挑战，例如在新兴短视频平台中，用户请求模式随时间及跨边缘节点呈现显著变化。针对特定本地边缘缓存的直观解决方案是收集来自其他边缘缓存的更多请求历史。然而，由于不同边缘节点上的内容分布具有异质性，统一合并这些请求历史可能无法取得满意效果。为解决这一问题，我们提出一种协作边缘缓存框架。首先，我们设计了一种基于元学习的协作策略，以确保本地模型能够及时适应持续变化的内容流行度。随后，我们提出一种边缘采样方法，用于选择更具"价值"的相邻边缘节点参与本地训练。为评估所提框架，我们通过基于真实数据驱动的实验验证了其有效性：与其他基准方法相比，该方法将平均缓存命中率提升了最高$10.12\%$（归一化值）。