Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching
By Xiaofei Wang, Chenyang Wang, Xiuhua Li, V. Leung, Tarik Taleb. IEEE Internet of Things Journal, 2020
364 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/57c6ba16f4d6142aee91aabd34764c375b089247
How researchers see this paper
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In Artificial Intelligence
- Mingzhe Chen
- H. Vincent Poor
- Zhaohui Yang
- Shuguang Cui
- Mohamed M. Abdallah
- Kai‐Kit Wong
- Khaled A. Althelaya
- Walid Saad
In Computer Networks and Communications
- H. Vincent Poor
- Shuguang Cui
- Zhaohui Yang
- Kai‐Kit Wong
- Mingzhe Chen
- Rui Zhang
- Geoffrey Y. Li
- Robert Schober
In Electrical and Electronic Engineering
- H. Vincent Poor
- Mingzhe Chen
- Shuguang Cui
- Zhaohui Yang
- Kai‐Kit Wong
- Khaled A. Althelaya
- Walid Saad
- Rui Zhang
Citations in the corpus
- Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “[23,24], the goal of FRL is to enable wireless devices to remember what they and the other wireless devices have learned.”
- A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things (2020, background): “Hence, achieving secure and privacy-preserving data analysis in the EC-assisted paradigm based on distributed/federated learning strategies [102] without the leakage of the private training dataset is still an open research direction.”