A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing (2024)
Qiang, X., Chang, Z., Hu, Y., Liu, L., & Hämäläinen, T. (2024). Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing. IEEE Internet of Things Journal, Early online. https://doi.org/10.1109/jiot.2024.3479158
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Qiang, Xianke; Chang, Zheng; Hu, Yun; Liu, Lei; Hämäläinen, Timo
Lehti tai sarja: IEEE Internet of Things Journal
ISSN: 2372-2541
eISSN: 2372-2541
Julkaisuvuosi: 2024
Ilmestymispäivä: 11.10.2024
Volyymi: Early online
Kustantaja: Institute of Electrical and Electronics Engineers
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1109/jiot.2024.3479158
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Rinnakkaistallenteen verkko-osoite (pre-print): https://doi.org/10.48550/arXiv.2405.18707
Tiivistelmä
Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learning (FL) stands as one of the fundamental technologies facilitating collaborative model training locally and aggregation, while safeguarding the privacy of vehicle data in VEI. However, traditional FL faces challenges in adapting to vehicle heterogeneity, training large models on resource-constrained vehicles, and remaining susceptible to model weight privacy leakage. Meanwhile, split learning (SL) is proposed as a promising collaborative learning framework which can mitigate the risk of model wights leakage, and release the training workload on vehicles. SL sequentially trains a model between a vehicle and an edge cloud (EC) by dividing the entire model into a vehicle-side model and an EC-side model at a given cut layer. In this work, we combine the advantages of SL and FL to develop an Adaptive Split Federated Learning scheme for Vehicular Edge Computing (ASFV). The ASFV scheme adaptively splits the model and parallelizes the training process, taking into account mobile vehicle selection and resource allocation. Our extensive simulations, conducted on non-independent and identically distributed data, demonstrate that the proposed ASFV solution significantly reduces training latency compared to existing benchmarks, while adapting to network dynamics and vehicles’ mobility.
YSO-asiasanat: ajoneuvot; tekoäly; simulointi; koneoppiminen; laskennallinen tiede
Vapaat asiasanat: vehicular edge intelligence; federated learning; split learning; split federated learning; adaptive split model
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2024
Alustava JUFO-taso: 2
