A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-65310-1_23
Reference26 articles.
1. Anh, T.T., Luong, N.C., Niyato, D., Kim, D.I., Wang, L.C.: Efficient training management for mobile crowd-machine learning: a deep reinforcement learning approach. IEEE Wirel. Commun. Lett. 8(5), 1345–1348 (2019)
2. Bataineh, A.S., Mizouni, R., Bentahar, J., Barachi, M.E.: Toward monetizing personal data: a two-sided market analysis. Future Gener. Comput. Syst. 111, 435–459 (2020)
3. Bataineh, A.S., Mizounib, R., El Barachic, M., Bentahara, J.: Monetizing personal data: a two-sided market approach. Procedia Comput. Sci. 83, 472–479 (2016)
4. Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H.V., Cui, S.: A joint learning and communications framework for federated learning over wireless networks. arXiv preprint arXiv:1909.07972 (2019)
5. Dai, H., Zeng, X., Yu, Z., Wang, T.: A scheduling algorithm for autonomous driving tasks on mobile edge computing servers. Syst. Arch. 94, 14–23 (2019)
Cited by 23 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. EDITORS: Energy-aware Dynamic Task Offloading using Deep Reinforcement Transfer Learning in SDN-enabled Edge Nodes;Internet of Things;2024-04
2. A Survey on Explainable Artificial Intelligence for Cybersecurity;IEEE Transactions on Network and Service Management;2023-12
3. A Review of Client Selection Methods in Federated Learning;Archives of Computational Methods in Engineering;2023-11-01
4. A Federated Learning Approach to Banking Loan Decisions;2023 International Symposium on Networks, Computers and Communications (ISNCC);2023-10-23
5. Coalitional Federated Learning: Improving Communication and Training on Non-IID Data With Selfish Clients;IEEE Transactions on Services Computing;2023-07-01
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3