Research Article
Value-Aware Collaborative Data Pricing for Federated Learning in Vehicular Networks
@INPROCEEDINGS{10.1007/978-3-030-98005-4_21, author={Yilong Hui and Jie Hu and Xiao Xiao and Nan Cheng and Tom H. Luan}, title={Value-Aware Collaborative Data Pricing for Federated Learning in Vehicular Networks}, proceedings={Ad Hoc Networks and Tools for IT. 13th EAI International Conference, ADHOCNETS 2021, Virtual Event, December 6--7, 2021, and 16th EAI International Conference, TRIDENTCOM 2021, Virtual Event, November 24, 2021, Proceedings}, proceedings_a={ADHOCNETS \& TRIDENTCOM}, year={2022}, month={3}, keywords={Federated learning Vehicular networks Data pricing Coalition game}, doi={10.1007/978-3-030-98005-4_21} }- Yilong Hui
Jie Hu
Xiao Xiao
Nan Cheng
Tom H. Luan
Year: 2022
Value-Aware Collaborative Data Pricing for Federated Learning in Vehicular Networks
ADHOCNETS & TRIDENTCOM
Springer
DOI: 10.1007/978-3-030-98005-4_21
Abstract
Vehicular federated learning (VFL) is a new paradigm that enables the use of data for distributed training under the premise of protecting the privacy of vehicular nodes (VNs). However, due to the heterogeneity of federated learning data, it is a challenge to evaluate the value of data and design an intelligent pricing scheme to effectively motivate the VNs in the vehicular networks (VNets) to complete learning tasks collaboratively. To this end, in this paper, we consider the value of data and propose a value-aware collaborative data pricing scheme for VFL. In the scheme, we first design a data transaction architecture based on the value of data and the cooperation among VNs. Then, by considering the non-independent and identically distributed (non-IID) degree and the age of data (AoD), we develop the data value model to evaluate the quality of data. Next, based on the requirement of the learning task and the data owned by each VN, we formulate the cooperation of the VNs as a coalition game, where the equilibrium of the coalition game is obtained by designing a distributed coalition formation algorithm. The simulation results show that the proposed scheme can lead to higher utility than the traditional methods.
