Incentivizing Semisupervised Vehicular Federated Learning: A Multidimensional Contract Approach With Bounded Rationality

计算机科学 激励 原始数据 激励相容性 困境 趋同(经济学) 机器学习 人工智能 有限理性 契约论 基线(sea) 哲学 海洋学 新古典经济学 认识论 地质学 微观经济学 经济 程序设计语言 经济增长
作者
Dongdong Ye,Xumin Huang,Yuan Wu,Rong Yu
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:9 (19): 18573-18588 被引量:15
标识
DOI:10.1109/jiot.2022.3161551
摘要

To facilitate the implementation of deep learning-based vehicular applications, vehicular federated learning is introduced by integrating vehicular edge computing with the newly emerged federated learning technology. In vehicular federated learning, it is widely considered that the raw data collected by vehicles have complete ground-truth labels. This, however, is not realistic and inconsistent with the current applications. To deal with the above dilemma, a semisupervised vehicular federated learning (Semi-VFL) framework is proposed. In the framework, each vehicular client uses labeled data shared by an application provider, and its own unlabeled data to cooperatively update a global deep neural network model. Furthermore, the application provider combines the multidimensional contract theory with prospect theory (PT) to design an incentive mechanism to stimulate appropriate vehicular clients to participate in Semi-VFL. Multidimensional contract theory is used to deal with the information asymmetry scenario where the application provider is not aware of vehicular clients' 3-D cost information, while PT is used to model the application provider's risk-aware behavior and make the incentive mechanism more acceptable in practice. After that, a closed-form solution for the optimal contract items under PT is derived. We present the real-world experimental results to demonstrate that Semi-VFL achieves the advantages in both the test accuracy and convergence speed, in comparison with existing baseline schemes. Based on the experimental results, we further perform the simulations to verify that our incentive mechanism is efficient.
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