Summary of Dynamic Scheduling For Vehicle-to-vehicle Communications Enhanced Federated Learning, by Jintao Yan et al.
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
by Jintao Yan, Tan Chen, Yuxuan Sun, Zhaojun Nan, Sheng Zhou, Zhisheng Niu
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT)
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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 This paper introduces vehicular federated learning (VFL) for edge training in connected vehicles, leveraging vehicle-to-vehicle (V2V) communications to enhance training efficiency. The authors formulate a stochastic optimization problem to optimize VFL training performance, considering energy constraints and mobility. They propose a V2V-enhanced dynamic scheduling (VEDS) algorithm to solve it, utilizing derivative-based drift-plus-penalty methods and convex optimization techniques. Experimental results show improved image classification accuracy on CIFAR-10 by 3.18% and reduced average displacement errors on Argoverse trajectory prediction dataset by 10.21%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vehicular federated learning is a new way to train machines that helps connected vehicles work better together. The paper talks about how to make this training more efficient by using the unique characteristics of vehicular networks, like direct communication between cars. They use math and computer science to create an algorithm that solves a tricky optimization problem. This algorithm makes training faster and more accurate, which is important for things like image recognition and predicting where other cars will go. |
Keywords
» Artificial intelligence » Federated learning » Image classification » Optimization
