首页|A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

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Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent chal-lenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players'decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

Federated learningPrivacy preservationEnergy optimizationGame theoryDistributed communication systems

Lihua Yin、Sixin Lin、Zhe Sun、Ran Li、Yuanyuan He、Zhiqiang Hao

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Cyberspace Institute of Advanced Technology,Guangzhou University,Guangzhou,510006,China

School of Cyber Science and Engineering,Huazhong University of Science and Technology,Wuhan,430074,China

China Industrial Control Systems Cyber Emergency Response Team,Beijing 100040,China

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaMajor Research Plan of the National Natural Science Foundation of ChinaGuangdong Basic and Applied Basic Research FoundationChina Postdoctoral Science FoundationZhejiang LabGuangzhou Science and Technology Plan Project

2018YFB21004006200207761872100921672032020A15151103852022M7108602020NF0AB01202102010440

2024

10.1016/j.dcan.2022.12.024

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(2)
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