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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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
