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Federated Double Deep Q-learning for Joint Delay and Energy Minimization
  in IoT networks

Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks

2 April 2021
S. Zarandi
Hina Tabassum
    FedML
ArXivPDFHTML

Papers citing "Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks"

3 / 3 papers shown
Title
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and
  Applications
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
Azim Akhtarshenas
Mohammad Ali Vahedifar
Navid Ayoobi
B. Maham
Tohid Alizadeh
Sina Ebrahimi
David López-Pérez
FedML
36
5
0
08 Oct 2023
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey
  of Vulnerabilities, Datasets, and Defenses
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
M. Ferrag
Othmane Friha
B. Kantarci
Norbert Tihanyi
Lucas C. Cordeiro
Merouane Debbah
Djallel Hamouda
Muna Al-Hawawreh
K. Choo
27
43
0
17 Jun 2023
AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and
  Future Directions
AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
Sundas Iftikhar
S. Gill
Chenghao Song
Minxian Xu
M. Aslanpour
...
Félix Cuadrado
Blesson Varghese
Omer F. Rana
Schahram Dustdar
Steve Uhlig
39
132
0
09 Dec 2022
1