mlsanityData to 14 Oct 2026

Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing

arXiv 2405.18707 ↗First posted 29 May 2024cs.LG cs.AI cs.NIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

27 of 54 references.

References · 27

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15 more
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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  2. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  3. 2025Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditabilitycited by 0