Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing

• URL: http://arxiv.org/abs/2405.18707v1
• Date: Wed, 29 May 2024 02:34:38 GMT
• Title: Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing
• Authors: Xianke Qiang, Zheng Chang, Yun Hu, Lei Liu, Timo Hamalainen,
• Abstract summary: Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems. Federated learning (FL) is one of the fundamental technologies facilitating collaborative model training locally and aggregation. We develop an Adaptive Split Federated Learning scheme for Vehicular Edge Computing (ASFV)
• Score: 6.004901615052089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning [2.6322811557798746]
  vehicular radio environment map federated learning (VREM-FL) is proposed. It combines mobility of vehicles with 5G radio environment maps. VREM-FL can be tuned to trade training time for radio resource usage.
  arXiv  Detail & Related papers  (2023-11-30T17:38:54Z)
• Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving [67.78611905156808]
  This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning framework. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model.
  arXiv  Detail & Related papers  (2023-06-28T12:44:59Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• Model-Based Reinforcement Learning with Isolated Imaginations [61.67183143982074]
  We propose Iso-Dream++, a model-based reinforcement learning approach. We perform policy optimization based on the decoupled latent imaginations. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild.
  arXiv  Detail & Related papers  (2023-03-27T02:55:56Z)
• Time-sensitive Learning for Heterogeneous Federated Edge Intelligence [52.83633954857744]
  We investigate real-time machine learning in a federated edge intelligence (FEI) system. FEI systems exhibit heterogenous communication and computational resource distribution. We propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model.
  arXiv  Detail & Related papers  (2023-01-26T08:13:22Z)
• MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles [21.615151912285835]
  We consider a base station coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner. Due to the mobility of vehicles, the connections between the base station and ICVs are short-lived. We propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations.
  arXiv  Detail & Related papers  (2022-12-07T08:53:53Z)
• Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles [14.80476265018825]
  Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems. We introduce C-DFL (Consensus based Decentralized Federated Learning) to tackle federated learning on connected vehicles.
  arXiv  Detail & Related papers  (2022-09-22T01:21:23Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles [29.476152044104005]
  We propose a novel online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories. On-board processors can train machine learning models using the collected data.
  arXiv  Detail & Related papers  (2022-05-17T19:14:38Z)
• A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles [14.068813113859338]
  Federated Deep Learning (FDL) is helping to realize distributed machine learning in the Internet of Vehicles (IoV) This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework.
  arXiv  Detail & Related papers  (2021-08-09T12:33:54Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.