Heterogeneous fairness algorithm based on federated learning in intelligent transportation system

Article type: Research Article

Authors: Jiang, Yue^a | Xu, Gaochao^a | Fang, Zhiyi^a | Song, Shinan^{a; *} | Li, Bingbing^b

Affiliations: [a] College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China | [b] JiLin Business and Technology College, Changchun, Jilin 130000, China

Correspondence: [*] Corresponding author: Shinan Song, College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China. E-mail: songshinan1990@163.com.

Abstract: With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ, the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%.

Keywords: Federated learning, heterogeneous system, intelligent transportation system, distributed learning

DOI: 10.3233/JCM-214991

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 21, no. 5, pp. 1365-1373, 2021