Bilateral Pricing for Dynamic Association in Federated Edge Learning

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成果类型：

期刊论文

作者：

Pan, Bangqi;Lu, Jianfeng;Cao, Shuqin;Liu, Jing;Tian, Wenlong;...

通讯作者：

Lu, JF

作者机构：

[Cao, Shuqin; Liu, Jing; Lu, Jianfeng; Pan, Bangqi; Lu, JF] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Rea, Wuhan 430065, Peoples R China.

[Cao, Shuqin; Liu, Jing; Lu, Jianfeng; Pan, Bangqi; Lu, JF] Dalian Univ Technol, Key Lab Social Comp & Cognit Intelligence, Minist Educ, Dalian 116024, Peoples R China.

[Tian, Wenlong] Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.

[Li, Minglu] Zhejiang Normal Univ, Sch Comp Sci & Technol, Jinhua 321004, Peoples R China.

通讯机构：

[Lu, JF ] W

Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Hubei Prov Key Lab Intelligent Informat Proc & Rea, Wuhan 430065, Peoples R China.

Dalian Univ Technol, Key Lab Social Comp & Cognit Intelligence, Minist Educ, Dalian 116024, Peoples R China.

语种：

英文

关键词：

Servers;Mobile handsets;Costs;Computational modeling;Pricing;Training;Performance evaluation;Optimization;Collaboration;Adaptation models;Federated edge learning (FEL);incentive mechanism;bargaining model;mobile edge computing

期刊：

IEEE TRANSACTIONS ON MOBILE COMPUTING

ISSN：

1536-1233

年：

2025

卷：

期：

页码：

1-14

DOI：

10.1109/TMC.2025.3527048

基金类别：

National Natural Science Foundation of China [62072411, 62372343, 62402352, 62402208]; Zhejiang Provincial Natural Science Foundation of China [LR21F020001]; Key Research and Development Program of Hubei Province [2023BEB024]; Open Fund of Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education [SCCI2024TB02]

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本校为其他机构

院系归属：

计算机科学与技术学院

摘要：

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, ...

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Bilateral Pricing for Dynamic Association in Federated Edge Learning

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