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Title	An Enhanced Incentive Mechanism For Crowdsourced Federated Learning Based On Contract Theory And Shapley Value
ID_Doc	8048
Authors	Dang T.K.; Tran-Truong P.T.; Trang N.T.H.
Year	2023
Published	Communications in Computer and Information Science, 1925 CCIS
DOI	http://dx.doi.org/10.1007/978-981-99-8296-7_2
Abstract	Federated learning is a recently dominant learning method for crowdsourced learning systems with diverse scales. It plays a pivotal role in smart city operation technologies, such as cross-organization (e.g., hospitals, banks, etc.), Mobile Ad hoc networks (MANETs), Mobile Edge Computing (MEC), Vehicle Ad hoc Networks (VANETs), and Internet of Things (IoTs). Specifically, this method aggregates a global model from local models trained on the private data of clients. To achieve high accuracy and collaborate effectively, federated learning-based crowdsourced systems need to attract sufficient quality clients. Therefore, a proper incentive mechanism is essential to motivate clients to join and contribute to the best of their ability. However, it is challenging to design such a mechanism due to the fact that each client has different system resources, data size, and effort. This implies that if the incentive mechanism is not well-designed, it will lead to a moral hazard situation, where clients may free-ride and the overall accuracy of the global model will undergo a downward spiral. Furthermore, the clients who contribute most to the accuracy of the global model are not necessarily those with the most decorated power and dedicated work. To address these challenges, we propose a joint optimization mechanism that leverages contract theory and Shapley value. This mechanism helps to reveal private information about clients and quantify their contribution to the global model, so that a suitable and equitable incentive can be constituted for each client. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.
Author Keywords	Contract Theory; Federated Learning; Incentive Mechanism; Shapley Value

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