Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Federated reinforcement learning"'
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 158, Iss , Pp 109980- (2024)
To schedule power sources operated by different entities in a short-time scale considering nonconvex generation cost and deep peak regulation (DPR) service constraints, this paper proposes an FRL-based multiple power sources coordination framework in
Externí odkaz:
https://doaj.org/article/9e5425994d814b8ba7182f975493a554
Publikováno v:
IEEE Open Journal of Vehicular Technology, Vol 5, Pp 1400-1440 (2024)
The increasing popularity of Internet of Things (IoT)-based wireless services highlights the urgent need to upgrade fifth-generation (5G) wireless networks and beyond to accommodate these services. Although 5G networks currently support a variety of
Externí odkaz:
https://doaj.org/article/12d76ca06a6048b5ae8cb37272243020
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 5, Pp 1222-1242 (2024)
In the midst of rising global population and environmental challenges, smart agriculture emerges as a vital solution by integrating advanced technologies to optimize agricultural practices. Through data-driven insights and automation, it tackles the
Externí odkaz:
https://doaj.org/article/7de61b4689d84e6cadc2edd5a6591fce
Publikováno v:
Alexandria Engineering Journal, Vol 86, Iss , Pp 56-66 (2024)
The exponential proliferation of wearable medical apparatus and healthcare information within the framework of the Internet of Medical Things (IoMT) introduces supplementary complexities pertaining to the elevated Quality of Service (QoS) of intellig
Externí odkaz:
https://doaj.org/article/e0e3ffb08ca94aef9d7f91d070cce0f0
Autor:
Jaewon Jeong, Joohyung Lee
Publikováno v:
Sensors, Vol 24, Iss 21, p 7031 (2024)
This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in
Externí odkaz:
https://doaj.org/article/564f0ea0ff2d43199784586a7ea69e83
Autor:
Woonghee Lee
Publikováno v:
ICT Express, Vol 9, Iss 5, Pp 803-808 (2023)
Federated reinforcement learning (FRL) has recently received a lot of attention in various fields. In FRL systems, the concept of performing more proper actions with better experiences exists, and we focused on this unique characteristic. Motivated b
Externí odkaz:
https://doaj.org/article/e5046e588e02435ba28c29d3aa7da3b4
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-13 (2023)
Abstract Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are widely employed in cloud computing, disaster relief, and various applications. How to quickly and efficiently deploy multi-AeBSs for higher capacity g
Externí odkaz:
https://doaj.org/article/5e08997bfd5e45f090b81b8be2acaee3
Federated Reinforcement Learning for Collaborative Intelligence in UAV-Assisted C-V2X Communications
Autor:
Abhishek Gupta, Xavier Fernando
Publikováno v:
Drones, Vol 8, Iss 7, p 321 (2024)
This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial veh
Externí odkaz:
https://doaj.org/article/c063d7b369ba45448c619ef147e868cb
Autor:
Xavier Fernando, Abhishek Gupta
Publikováno v:
Drones, Vol 8, Iss 6, p 238 (2024)
The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated lea
Externí odkaz:
https://doaj.org/article/98c3e683b0ff4b2ea47c66d05f28e33f