Summary of Mobility-aware Federated Learning: Multi-armed Bandit Based Selection in Vehicular Network, by Haoyu Tu et al.
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network
by Haoyu Tu, Lin Chen, Zuguang Li, Xiaopei Chen, Wen Wu
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a mobility-aware vehicular federated learning (MAVFL) scheme for vehicle selection in FL over vehicular networks. It designs a vehicle selection algorithm using real-time successful training participation ratios to minimize utility functions considering training loss and delay. The proposed scheme shows better training performance with approximately 28% faster convergence compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how vehicles can help train machine learning models together while moving around, which is called federated learning. The authors designed a system that chooses the right vehicles for this process based on how well they do in training the model. They tested their method and found it was better than others with about 28% faster results. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
