Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing
Xianke Qiang, Zheng Chang, Yun Hu, Lei Liu, Timo Hamalainen
TL;DR
The paper tackles privacy-preserving distributed learning in VEI by fusing split learning with federated learning into Adaptive Split Federated Learning for Vehicular Edge Computing (ASFV). It introduces online adaptive cut-layer selection, vehicle-aware resource management, and a joint BCD-based optimization to minimize training latency under energy and mobility constraints, presenting formulations that couple computation, communication, and cut-layer decisions. The authors prove convergence under standard assumptions and validate ASFV via simulations on non-IID MNIST, Fashion-MNIST, and CIFAR-10 data, showing reduced latency and energy with accuracy close to sequential SL and better scalability than FL/SFL baselines. The approach offers practical impact for real-time VEI deployments by balancing training speed, energy consumption, and privacy in highly dynamic vehicular networks.
Abstract
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.