Swarm Reinforcement Learning for Collaborative Content Caching in Information Centric Networks

Jiajin Yang, Lixing Chen, Junhua Tang, Jianhua Li, Wu Yang

Abstract: In-network content caching is a fundamental functionality in Information Centric Network (ICN), which facilitates content distribution with reduced bandwidth consumption, lower network congestion, and faster content retrieval. However, traditional heuristic caching strategies often fail to handle dynamic ICN environments, and most leaning-based strategies cannot realize secured content caching for distributed ICN. In observation of these challenges, this paper investigates collaborative content caching in ICN, and proposes a novel algorithm, called Swarm Reinforcement Learning (SRL), for designing a secured caching mechanism in distributed ICN caching platform. SRL inherits salients features from both Swarm Learning (SL) and Deep Reinforcement Learning (DRL): it enables a fully decentralized learning process and strictly guarantees the privacy and security of data and models during collaborative caching by leveraging the blockchain technique; SRL also lets local ICN router interact with the local environment and integrate the learned knowledge with other ICN routers for constructing a collaborative caching strategy that maximizes the long-term reward of the entire ICN caching platform. We carry out systematic experiments to evaluate the performance of the proposed method. The results show that SRL-based collaborative caching outperforms state-of-the-art caching strategies in terms of cache hit rate and content retrieval delay, and also improves the stability of the ICN caching platform.