Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits

Author:

Li TanORCID,Song LinqiORCID

Funder

Hong Kong Research Grants Council

Technological Breakthrough Project of Science, Technology and Innovation Commission of Shenzhen Municipality

Guangdong Basic and Applied Basic Research Foundation under Key Project

Shenzhen Science and Technology Funding Fundamental Research Program

Hong Kong Laboratory for AI-Powered Financial Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Reference25 articles.

1. Federated learning: Strategies for improving communication efficiency;koneáný;arXiv 1610 05492,2016

2. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization;reisizadeh;Proc Int Conf Artif Intell Statist,2020

3. Deep Learning with Differential Privacy

4. Concentrated differentially private and utility preserving federated learning;hu;arXiv 2003 13761,2020

5. A contextual-bandit approach to personalized news article recommendation

Cited by 16 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. Caching User-Generated Content in Distributed Autonomous Networks via Contextual Bandit;IEEE Transactions on Mobile Computing;2024-08

2. Low power decentralized differentially private multi-armed bandit algorithm based performance improvement on long-range radio network;Wireless Networks;2024-07-15

3. Vertical Federated Learning: Concepts, Advances, and Challenges;IEEE Transactions on Knowledge and Data Engineering;2024-07

4. A Multi - User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach;2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2023-12-17

5. Reinforcing Industry 4.0 With Digital Twins and Blockchain-Assisted Federated Learning;IEEE Journal on Selected Areas in Communications;2023-11

同舟云学术

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