| Texto completo | |
| Autor(es): |
Fittipaldi, Giuliano
;
Couto, Rodrigo S.
;
Costa, Luis Henrique M. K.
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 20TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS, WIMOB; v. N/A, p. 6-pg., 2024-01-01. |
| Resumo | |
The emergence of software-defined vehicles has brought machine learning into the vehicular domain. To support these data-driven applications, techniques to incentivize users to share their vehicle data are crucial. Federated learning trains machine learning models in a distributed manner, leveraging client data without compromising its privacy. Nonetheless, in vehicular networks, the dynamic behavior of nodes affects client availability and the global model's performance. Accordingly, this paper evaluates federated learning (FL) in a realistic vehicular network topology, accounting for real vehicle traffic in two Brazilian urban areas. The network simulation covers 3.7 km(2) with road speeds and 1,290 vehicles per hour, based on real data. We observe a performance decay in urban areas with longer vehicle permanence. Interestingly, longer vehicle participation in FL training leads to a biased model, with reduced generalization. We then improve our investigation based on the Dice-Sorensen coefficient to enhance vehicle variability over time. With 47% fewer vehicles per round, we achieve faster learning, higher convergence in the first 15 rounds, and equivalent final accuracy of 93%. (AU) | |
| Processo FAPESP: | 23/00811-0 - EcoSustain: ciência de dados e computação para o meio ambiente |
| Beneficiário: | Antonio Jorge Gomes Abelém |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 23/00673-7 - Inteligência distribuída em redes de comunicação e internet das coisas |
| Beneficiário: | Nelson Luis Saldanha da Fonseca |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |