RTIFed: A Reputation based Triple-step Incentive mechanism for energy-aware Federated learning over battery-constricted devices

doi:10.1016/j.comnet.2024.110192

	RTIFed: A Reputation based Triple-step Incentive mechanism for energy-aware Federated learning over battery-constricted devices
	Wen, Tian 1,2; Zhang, Hanqing 1; Zhang, Han 1; Wu, Huixin 1; Wang, Danxin 1; Liu, Xiuwen 1; Zhang, Weishan 1; Wang, Yuwei 2; Cao, Shaohua 1
	2024-03-01
发表期刊	COMPUTER NETWORKS
ISSN	1389-1286
卷号	241 页码:14
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.
关键词	Federated learning Incentive mechanism Energy efficiency Client activation Stackelberg game
DOI	10.1016/j.comnet.2024.110192
收录类别	SCI
语种	英语
资助项目	National Natural Science Foun-dation of China (NSFC)[62072469]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001172864500001
出版者	ELSEVIER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38835
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.China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, 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.