数字通信与网络（英文）2025，Vol.11Issue(6)：1809-1821,13.DOI:10.1016/j.dcan.2025.05.015
Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment
Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment
Sowmya Madhavan 1M.G.Aruna 2G.P.Ramesh 3Dhulipalla Ramya Krishna4
作者信息
- 1. Department of Electronics and Communication Engineering,Nitte Meenakshi Institute of Technology,Yelahanka,Bangalore,India
- 2. Department of Artificial Intelligence and Machine Learning,Dayanand Sagar College of Engineering,Bangalore,India
- 3. Department of Electronics and Communication Engineering,St Peter's Institute of Higher Education and Research,Chennai,India
- 4. Department of Computer Science and Engineering,Koneru Lakshmaiah Education Foundation,Hyderabad,India
- 折叠
摘要
关键词
Cybertwin/Federated learning/ResNet50/Resource allocation/Scaled exponential linear unit/Weighted proximal policyKey words
Cybertwin/Federated learning/ResNet50/Resource allocation/Scaled exponential linear unit/Weighted proximal policy引用本文复制引用
Sowmya Madhavan,M.G.Aruna,G.P.Ramesh,Dhulipalla Ramya Krishna..Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment[J].数字通信与网络（英文）,2025,11(6):1809-1821,13.
