Summary of Distributed Client Selection with Multi-objective in Federated Learning Assisted Internet Of Vehicles, by Narisu Cha and Long Chang
Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles
by Narisu Cha, Long Chang
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 distributed client selection scheme to reduce the cost of maintaining an active state for participants in federated learning frameworks for the Internet of Vehicles (IoV). The scheme, based on fuzzy logic, selects clients with the highest evaluation among neighbors by considering four variables: sample quantity, throughput available, computational capability, and local dataset quality. This approach approximates centralized client selection in terms of accuracy while significantly reducing communication overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps reduce the cost of maintaining an active state for millions of vehicles willing to train a model in IoV. It proposes a way to choose the best neighbors to participate and share their knowledge, making it more efficient. This is useful because many vehicles want to contribute, but it’s expensive for all of them to do so. |
Keywords
* Artificial intelligence * Federated learning
