Federated deep reinforcement learning for Internet of Things with decentralized cooperative edge caching
Wang, Xiaofei; Wang, Chenyang; Li, Xiuhua; Leung, Victor C. M.; Taleb, Tarik (2020-04-09)
X. Wang, C. Wang, X. Li, V. C. M. Leung and T. Taleb, "Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching," in IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9441-9455, Oct. 2020, doi: 10.1109/JIOT.2020.2986803
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https://urn.fi/URN:NBN:fi-fe2020111790852
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Abstract
Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.
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