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AI Research
arXiv CS.AI·Jun 9·5 min read
EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning
TL;DR
Federated learning struggles with slow convergence and robustness due to diverse client data and systems. EvoCSFL, a surrogate-assisted client evolutionary selection framework, was developed to enhance client selection.
✦ Why It Matters
Engineers can implement EvoCSFL to improve the efficiency and robustness of their federated learning systems.
Key Takeaways
- Utilize EvoCSFL for improved client selection in federated learning.
- Adopt surrogate models to predict client performance efficiently.
- Focus on reducing energy consumption during model training.
- Experiment with various datasets to validate model robustness.
How It Works
EvoCSFL employs a two-step process for client selection. First, it generates candidate client sets using traditional selection strategies.
Then, it evaluates these candidates through a metric that combines model performance, communication latency, and energy consumption. A surrogate model approximates the performance of these selections, allowing an evolutionary algorithm to efficiently navigate the selection space and identify optimal client subsets.