Heterogeneous fairness algorithm based on federated learning in intelligent transportation system
| Autor: | Yue Jiang, Bingbing Li, Zhiyi Fang, Shinan Song, Gaochao Xu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computational Mathematics
Computer science Distributed computing 010401 analytical chemistry 0202 electrical engineering electronic engineering information engineering General Engineering 020206 networking & telecommunications 02 engineering and technology 01 natural sciences Intelligent transportation system Federated learning 0104 chemical sciences Computer Science Applications |
| Zdroj: | Journal of Computational Methods in Sciences and Engineering. 21:1365-1373 |
| ISSN: | 1875-8983 1472-7978 |
| DOI: | 10.3233/jcm-214991 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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