中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
RTIFed: A Reputation based Triple-step Incentive mechanism for energy-aware Federated learning over battery-constricted devices

文献类型：期刊论文

作者Wen, Tian1,2; Zhang, Hanqing2; Zhang, Han2; Wu, Huixin2; Wang, Danxin2; Liu, Xiuwen2; Zhang, Weishan2; Wang, Yuwei1; Cao, Shaohua2
刊名COMPUTER NETWORKS
出版日期2024-03-01
卷号241页码:14
关键词Federated learning Incentive mechanism Energy efficiency Client activation Stackelberg game
ISSN号1389-1286
DOI10.1016/j.comnet.2024.110192
英文摘要Federated Learning (FL) is an emerging field of research that contributes to collaboratively training machine learning models by leveraging idle computing resources and sensitive data scattered among massive IoT devices in a privacy -preserving manner without raw data exchange. The majority of existing research has concentrated on developing efficient learning algorithms that demonstrate superior learning performance. Despite the extraordinary advancement, FL encounters three challenges that need to be resolved jointly, specifically, (1) how to properly measure the client reliability and contribution that serve as the basis for compensation allocation, (2) how to reasonably activate reliable clients, whose dataset grows gradually, to avoid over -learning, and (3) how to efficiently formulate the optimal local training decisions to improve model performance and energy efficiency of battery -constricted devices. To address the above challenges, this paper proposes a Reputation based Triple -step Incentive mechanism of Federated learning (RTIFed), which (1) introduces reputation as the metric to measure client reliability and contribution while employing blockchain to accomplish decentralized and tamper -resistant reputation management, (2) activates clients with high reputation and informative data pursuant by a Richness -of -Information Activation Strategy (RIAS), (3) determines training epochs for each client based on a Stackelberg Game according to an Optimal Training Decision Strategy (OTDS). Numerical results clearly show that the proposed RTIFed effectively motivates high -quality clients and improves learning accuracy while reducing the energy cost to meet the low resource consumption need of battery -constricted devices in smart city FL scenarios.
资助项目National Natural Science Foun-dation of China (NSFC)[62072469]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:001172864500001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/38834]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cao, Shaohua
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao, Peoples R China
推荐引用方式
GB/T 7714		 Wen, Tian,Zhang, Hanqing,Zhang, Han,et al. RTIFed: A Reputation based Triple-step Incentive mechanism for energy-aware Federated learning over battery-constricted devices[J]. COMPUTER NETWORKS,2024,241:14.
APA Wen, Tian.,Zhang, Hanqing.,Zhang, Han.,Wu, Huixin.,Wang, Danxin.,...&Cao, Shaohua.(2024).RTIFed: A Reputation based Triple-step Incentive mechanism for energy-aware Federated learning over battery-constricted devices.COMPUTER NETWORKS,241,14.
MLA Wen, Tian,et al."RTIFed: A Reputation based Triple-step Incentive mechanism for energy-aware Federated learning over battery-constricted devices".COMPUTER NETWORKS 241(2024):14.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。