An Incentivized Federated Learning Model Based on Contract Theory

WANG Xin; LI Meiqing; WANG Liming; YU Yun; YANG Yang; SUN Lingyun

doi:10.11999/JEIT221081

Volume 45 Issue 3

Mar. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(3): 874-883

WANG Xin, LI Meiqing, WANG Liming, YU Yun, YANG Yang, SUN Lingyun. An Incentivized Federated Learning Model Based on Contract Theory[J]. Journal of Electronics & Information Technology, 2023, 45(3): 874-883. doi: 10.11999/JEIT221081

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WANG Xin, LI Meiqing, WANG Liming, YU Yun, YANG Yang, SUN Lingyun. An Incentivized Federated Learning Model Based on Contract Theory[J]. Journal of Electronics & Information Technology, 2023, 45(3): 874-883. doi: 10.11999/JEIT221081

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An Incentivized Federated Learning Model Based on Contract Theory

doi: 10.11999/JEIT221081

WANG Xin^{1, 4, 5
,
,},
LI Meiqing²,
WANG Liming²,
YU Yun³,
YANG Yang³,
SUN Lingyun^{4, 5}

1.
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2.
College of Computer Science and Technology, Xidian University, Xi’an 710071, China
3.
Digital Grid Research Institute Co. Ltd, China Southern Power Grid, Guangzhou 510663, China
4.
Zhejiang University-China Southern Power Grid Joint Research Centre on AI, Hangzhou 310058, China
5.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

Funds: The National Key Research and Development Program of China(2020YFB0906000, 2020YFB0906004)

Received Date: 2022-08-16
Rev Recd Date: 2023-03-02

Available Online: 2023-03-03

Publish Date: 2023-03-10

Abstract

Abstract

In view of the fact that there is rare research on the incentive mechanism design in decentralized federated learning, and the existing incentive mechanisms for federated learning are seldom based on the global model effect, an incentive mechanism based on contract theory, is added into decentralized federated learning and a new incentivized federated learning model is proposed. A blockchain and an InterPlanetary File System (IPFS) are used to replace the central server of traditional federated learning for model parameter storage and distribution, based on which a contract publisher is responsible for contract formulation and distribution, and each federated learning participant chooses to sign a contract based on its local data quality. The contract publisher evaluates each local training model after each round of local training and issues a reward based on the agreed-upon conditions in the contract. The global model aggregation also aggregates model parameters based on the reward results. Experimental validation on the MNIST dataset and industry electricity consumption dataset show that the proposed incentivized federated learning model outperforms traditional federated learning and its decentralized structure improves its robustness.
- Federated learning,
- Incentive mechanism,
- Contract theory,
- Decentralization,
- Power big data

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References(27)

References

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