A federated reinforcement learning framework for incumbent technologies in beyond 5G networks
… proposes a federated RL-based channel resource allocation framework for 5G/ B5G networks,
… Currently, over 70 percent of mobile data traffic is generated by WLAN networks [1], and …
… Currently, over 70 percent of mobile data traffic is generated by WLAN networks [1], and …
Federated deep reinforcement learning for the distributed control of NextG wireless networks
P Tehrani, F Restuccia… - … Spectrum Access Networks …, 2021 - ieeexplore.ieee.org
… a multi-cell network with mobile users. The base … federated versions of these algorithm.
We demonstrate that our F-DRL approach improves the performance in terms of overall network …
We demonstrate that our F-DRL approach improves the performance in terms of overall network …
Federated deep reinforcement learning for recommendation-enabled edge caching in mobile edge-cloud computing networks
… edge caching system in mobile edge-cloud computing networks. The proposed system …
a federated discrete soft actor-critic (FDSAC) algorithm that only federates the critic network …
a federated discrete soft actor-critic (FDSAC) algorithm that only federates the critic network …
Federated deep reinforcement learning-based task offloading and resource allocation for smart cities in a mobile edge network
X Chen, G Liu - Sensors, 2022 - mdpi.com
Mobile edge computing (MEC) has become an … Since federated learning has the characteristics
of protecting … A two-timescale federated deep reinforcement learning algorithm based on …
of protecting … A two-timescale federated deep reinforcement learning algorithm based on …
Experience-driven computational resource allocation of federated learning by deep reinforcement learning
… ing the federated learning under dynamic network environment as a … federated learning
problems. Specifically, we adopt the deep reinforcement learning to decide how fast mobile …
problems. Specifically, we adopt the deep reinforcement learning to decide how fast mobile …
Resource allocation in mobility-aware federated learning networks: A deep reinforcement learning approach
… model of a Federated Learning Network (FLNet) as shown in Fig. 1. The network consists
of a … Miao, “Federated learning in mobile edge networks: A comprehensive survey,” arXiv …
of a … Miao, “Federated learning in mobile edge networks: A comprehensive survey,” arXiv …
Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mobile networks
… However, considering the very limited communication resources in high-speed mobile
networks, we design a so-called federated Bi-distillation mechanism, to lighten the model …
networks, we design a so-called federated Bi-distillation mechanism, to lighten the model …
Multi-agent federated reinforcement learning strategy for mobile virtual reality delivery networks
… We then employ federated multi-agent deep reinforcement learning (RL) to help … networks
deployed on edge servers. For the Federated Learning (FL) part, we present the federated …
deployed on edge servers. For the Federated Learning (FL) part, we present the federated …
… deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense network
… resource-intensive mobile and IoT … mobile and IoT devices is anticipated to have an explosive
growth in the 5G era, which will result in the congestion of the cloud computing network. In …
growth in the 5G era, which will result in the congestion of the cloud computing network. In …
Federated deep reinforcement learning for open ran slicing in 6g networks
A Abouaomar, A Taik, A Filali… - IEEE Communications …, 2022 - ieeexplore.ieee.org
… Each MVNO has a set of users that upload their packets to the network. We consider two
types of users, namely enhanced mobile broadband (eMBB) and ultra-reliable low-latency …
types of users, namely enhanced mobile broadband (eMBB) and ultra-reliable low-latency …
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