A4 Artikkeli konferenssijulkaisussa
Contract-Based Incentive Mechanism for Blockchain-Enabled Federated Learning in Vehicle Edge Computing (2023)

Xu, R., Chang, Z., Zhao, Z., & Min, G. (2023). Contract-Based Incentive Mechanism for Blockchain-Enabled Federated Learning in Vehicle Edge Computing. In GLOBECOM 2023 : 2023 IEEE Global Communications Conference (pp. 1812-1817). IEEE. IEEE Global Communications Conference. https://doi.org/10.1109/GLOBECOM54140.2023.10437342

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Xu, Runchen; Chang, Zheng; Zhao, Zhiwei; Min, Geyong

Emojulkaisu: GLOBECOM 2023 : 2023 IEEE Global Communications Conference

Konferenssin paikka ja aika: Kuala Lumpur, Malaysia, 4.-8.12.2023

ISBN: 979-8-3503-1091-7

eISBN: 979-8-3503-1090-0

Lehti tai sarja: IEEE Global Communications Conference

ISSN: 2334-0983

eISSN: 2576-6813

Julkaisuvuosi: 2023

Ilmestymispäivä: 04.12.2023

Artikkelin sivunumerot: 1812-1817

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/GLOBECOM54140.2023.10437342

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

Vehicular edge computing (VEC) has been introduced to bring powerful in-proximity computing solutions to vehicles. VEC is able to boost the development of vehicular networks by handling computing tasks and accommodating artificial intelligence (AI). To fulfill the requirements of low latency and security in realizing AI for vehicular networks, and fully utilize the vehicles' capabilities on sensing and computing, federated learning (FL) in VEC emerges as a potential solution. However, privacy protection, data security and information asymmetry issues pose challenges on efficiently and securely motivating more vehicles to participate in FL. Thus, this paper proposes a contract-based incentive mechanism for blockchain-enabled FL, which employs contract theory to establish an optimal contract design between VEC servers and vehicles. We present the necessary and sufficient conditions to obtain an optimal contract and analyze the simplification of the constraints. The simulation results show that our proposed method is effective in providing incentives and outperforms other benchmark schemes.

YSO-asiasanat: liikennetelematiikka; langattomat verkot; reunalaskenta; koneoppiminen; tietosuoja; lohkoketjut

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Alustava JUFO-taso: 1