Contribution Matching-Based Hierarchical Incentive Mechanism Design for Crowd Federated Learning

With the growing public attention to data privacy protection, the problem of data silos has been exacerbated, which makes it more difficult for crowd intelligence technologies to get off the ground. Meanwhile, Federated Learning (FL) has received great attention for its ability to break data silos and jointly build machine learning models. To crack the data silo problem in crowd intelligence, we propose a new Crowd Federated Learning (CFL) framework, which is a two-tier architecture consisting of a cloud server, model owners, and data collectors, that enables collaborative model training among individuals without the need for raw data interaction. However, existing work struggles to simultaneously ensure the balance of incentives among data collectors, model owners, and cloud server, which can affect the willingness of sharing and collaboration among subjects. To solve the above problem, we propose a hierarchical incentive mechanism named FedCom, i.e., Crowd
F
ederated Learning for
Co
ntribution
m
atching, to match participants’ contributions with rewards. We theoretically prove that FedCom has contribution matching fairness, and conduct extensive comparative experiments with five baselines on one simulated dataset and four real-world datasets. Experimental results validate that FedCom is able to reduce the computation time of contribution evaluation by about 8 times and improve the global model performance by about 2% while ensuring fairness.