Projects per year
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.
- Cooperative caching
- deep reinforcement learning (DRL)
- edge caching
- federated learning
- hit rate
- Internet of Things (IoT)
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Bagaa, M., El Marai, O., Maiouak, M., Bekkouche, O., Yang, B., Hellaoui, H., Taleb, T., Addad, R., Afolabi, I., Mada, B., Naas, S., Yu, H., Boudi, A., Kerfah, I., Benzaid, C., Amor, A., Sehad, N., Kianpisheh, S., Shokrnezhad, M. & Maity, I.
01/09/2017 → 31/08/2021
Project: Academy of Finland: Other research funding