Contract Theory Based Incentive Mechanism for Federated Learning in Health CrowdSensing

Federated Learning provides an effective solution for multi-party data processing under privacy-preserving, and becomes a good choice for crowd intelligence extraction in Health CrowdSensing. The quality of the local model submitted by the data holder determines the quality of the global model in Fe...

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Bibliographic Details
Published in:IEEE internet of things journal 2023-03, Vol.10 (5), p.1-1
Main Authors: Li, Li, Yu, Xi, Cai, Xuliang, He, Xin, Liu, Yanhong
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biological system modeling
Computation
Computational modeling
Contract Theory
Contracts
Costs
Data acquisition
Data models
Data processing
Federated learning
Health CrowdSensing
Incentive Mechanism
Optimization
Privacy-preserving
Training
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Summary:Federated Learning provides an effective solution for multi-party data processing under privacy-preserving, and becomes a good choice for crowd intelligence extraction in Health CrowdSensing. The quality of the local model submitted by the data holder determines the quality of the global model in Federated Learning, and the quality of the local model depends on the data quantity, data quality and computing power of the data holder. However, in the process of model training, the data holder will inevitably spend the cost of communication and local model training, and higher quality data acquisition and higher quality local model training require higher cost. Therefore, how to motivate data holders with a large amount of high-quality data and computing power to participate in Federated Learning has become an urgent problem to be solved. This article transforms the problem of motivating data holders into an optimization problem of utility from the perspective of maximizing the utility of the data holder, establishes the incentive mechanism based on the Contract Theory, and proves that the optimal strategy set of the data holders reaches Nash Equilibrium. A large number of experiments based on public datasets of UCI and MNIST verify that the incentive mechanism can make the baseline algorithm converge faster, while resisting malicious behaviors such as free-riding and collusive attacks. Furthermore, the data holder with a large amount of high-quality data and computing power can obtain higher revenue.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3218008