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Summary of Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing, by Xianke Qiang et al.


Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing

by Xianke Qiang, Zheng Chang, Yun Hu, Lei Liu, Timo Hamalainen

First submitted to arxiv on: 29 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The Adaptive Split Federated Learning scheme for Vehicular Edge Computing (ASFV) combines federated learning and split learning to enable collaborative model training on resource-constrained vehicles. The proposed approach adaptively splits the model, parallelizes the training process, and considers mobile vehicle selection and resource allocation. This solution significantly reduces training latency compared to existing benchmarks while adapting to network dynamics and vehicle mobility.
Low GrooveSquid.com (original content) Low Difficulty Summary
Vehicular edge intelligence is a new way to make cars smarter by using artificial intelligence at the car’s edge computing system. Federated learning helps cars learn together without sharing personal data. But, this technology has some challenges like adapting to different cars, training big models on small devices, and keeping model weights private. Split learning can help solve these problems by dividing the model into two parts: one for the car and one for the edge cloud. In this project, researchers combined federated and split learning to create a new approach called Adaptive Split Federated Learning (ASFV). This solution is faster than other methods and can handle changes in network and cars’ movement.

Keywords

» Artificial intelligence  » Federated learning