Research on Hierarchical Federated Learning Incentive Mechanism Based on Master-Slave Game

JIA Yunjian; HUANG Yu; LIANG Liang; WAN Yangliang; ZHOU Jihua

doi:10.11999/JEIT220175

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1366-1373

JIA Yunjian, HUANG Yu, LIANG Liang, WAN Yangliang, ZHOU Jihua. Research on Hierarchical Federated Learning Incentive Mechanism Based on Master-Slave Game[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1366-1373. doi: 10.11999/JEIT220175

Citation:

JIA Yunjian, HUANG Yu, LIANG Liang, WAN Yangliang, ZHOU Jihua. Research on Hierarchical Federated Learning Incentive Mechanism Based on Master-Slave Game[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1366-1373. doi: 10.11999/JEIT220175

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Research on Hierarchical Federated Learning Incentive Mechanism Based on Master-Slave Game

doi: 10.11999/JEIT220175

1.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2.
95696 Troops, Chongqing 400030, China
3.
Chongqing Key Laboratory of Complex Environment Communication, Chongqing Jinmei Communication Co. Ltd. Chongqing 400030, China

Funds: The National Natural Science Foundation of China (62071075, 61971077), The Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0704)

Received Date: 2022-02-25
Accepted Date: 2022-08-15
Rev Recd Date: 2022-06-27

Available Online: 2022-08-16

Publish Date: 2023-04-10

Abstract

Abstract

In order to optimize the training delay of the hierarchical Federated Learning (FL) global model, focusing on the selfishness of the terminal devices in the actual scene, an incentive mechanism based on game theory is proposed. Under the condition of limited incentive budget, the equilibrium solution between terminal devices and edge servers and the minimum edge model training delay are obtained. Considering the different number of terminal devices, a variable incentive training acceleration algorithm based on Stackelberg game is designed to minimize the training delay of a global model. Simulation results demonstrate that the proposed algorithm can effectively reduce the impact of terminal devices selfishness and improve the training speed of hierarchical federated learning global model.
- Hierarchical Federated Learning (FL),
- Game theory,
- Incentive mechanism

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References(15)

References

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