Federated Learning for Distributed Energy-Efficient Resource Allocation
Author:
Affiliation:
1. Queen Mary University of London,School of Electronic Engineering and Computer Science,London,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9837954/9838246/09882281.pdf?arnumber=9882281
Cited by 6 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. OFDMA-F²L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface;IEEE Transactions on Wireless Communications;2024-07
2. A Comprehensive Survey on Energy Efficiency in Federated Learning: Strategies and Challenges;2024 IEEE 8th Energy Conference (ENERGYCON);2024-03-04
3. Meta Federated Reinforcement Learning for Distributed Resource Allocation;IEEE Transactions on Wireless Communications;2024
4. Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges;IEEE Transactions on Emerging Topics in Computational Intelligence;2023-06
5. Energy-Efficient Task Offloading for Semantic-Aware Networks;ICC 2023 - IEEE International Conference on Communications;2023-05-28
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