EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning
arXiv:2606.07702v1 Announce Type: new Abstract: The heterogeneity of client data and systems makes it difficult to achieve satisfactory convergence speed and robustness in federated learning with random client selection. To address this issue, this paper proposes a surrogate-assisted client evolutionary selection framework for federated learning. In this framework, some typical client selection strategies are first used to generate candidate sets, and a metric function that integrates model performance, communication latency, and energy consumption is developed to formulate the client selectio
The increasing complexity and scale of federated learning deployments, combined with the heterogeneity of client data and systems, necessitate more efficient and robust client selection methods.
Improving the efficiency and robustness of federated learning is crucial for its wider adoption in privacy-sensitive and resource-constrained environments, impacting various industries leveraging distributed AI.
This research introduces a novel, surrogate-assisted evolutionary client selection framework that moves beyond random selection, potentially leading to faster convergence and more reliable federated AI models.
- · Federated Learning platforms
- · Edge AI providers
- · Sectors requiring privacy-preserving AI (e.g., healthcare, finance)
- · AI researchers
- · Traditional centralized machine learning approaches
- · Inefficient federated learning solutions
More applications will adopt federated learning due to improved performance and stability.
This could accelerate the development of AI agents that learn collaboratively and robustly across distributed devices.
The widespread adoption of efficient federated learning might reduce the immediate need for extensive centralized data aggregation, influencing data privacy regulations and infrastructure investments.
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