Meta Federated Reinforcement Learning for Distributed Resource Allocation
Author:
Affiliation:
1. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K.
2. Department of Electronic Engineering, Tsinghua University, Beijing, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/4656680/10376360.pdf?arnumber=10376360
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Resource Optimization for Semantic-Aware Networks With Task Offloading;IEEE Transactions on Wireless Communications;2024-09
2. How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making;Electronics;2024-03-29
3. FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks;IEEE Access;2024
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