Summary of A Green Multi-attribute Client Selection For Over-the-air Federated Learning: a Grey-wolf-optimizer Approach, by Maryam Ben Driss et al.
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach
by Maryam Ben Driss, Essaid Sabir, Halima Elbiaze, Abdoulaye Baniré Diallo, Mohamed Sadik
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel framework for Over-the-Air Federated Learning (FL) that addresses the challenges of deployment complexity and interoperability issues in heterogeneous scenarios. The framework employs the Grey Wolf Optimizer (GWO) to strategically select clients for each round, optimizing accuracy, energy, delay, reliability, and fairness constraints. The authors evaluate their approach against existing state-of-the-art methods, demonstrating notable improvements in model loss minimization, convergence time reduction, and energy efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train machine learning models without sharing sensitive data. It’s called Over-the-Air Federated Learning (FL). FL has many benefits, like keeping data private and reducing the need for a central server. But it also comes with some challenges, such as making sure devices can work together smoothly. The authors of this paper propose a new framework that uses an algorithm called Grey Wolf Optimizer to help devices work together more efficiently. They tested their approach against other methods and found that it was better at minimizing errors, converging quickly, and using less energy. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
