Edge Caching

Edge caching aims to improve the speed and reliability of content delivery by storing frequently accessed data closer to users at the network's edge. Current research emphasizes optimizing caching strategies using machine learning, particularly deep reinforcement learning and federated learning, often incorporating techniques like transfer learning and contrastive learning to enhance prediction accuracy and handle dynamic content popularity and user mobility. These advancements are crucial for addressing the increasing demands of next-generation networks, improving user experience, and reducing network congestion by minimizing backhaul traffic.