EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning

Researchers propose EvoCSFL, a machine learning framework that optimizes client selection in federated learning systems by using surrogate models and evolutionary algorithms. The method balances model performance, communication latency, and energy consumption to achieve faster convergence and improved robustness compared to random selection approaches.

Federated learning has emerged as a critical infrastructure for training machine learning models across distributed networks while preserving data privacy. However, the inherent heterogeneity in client capabilities and data quality creates substantial challenges for convergence speed and system robustness. EvoCSFL addresses this by moving beyond random client selection—a naive approach that wastes computational resources on underperforming nodes.

The framework's innovation lies in its three-layer approach: generating candidate selections through established strategies, constructing a surrogate model to predict subset performance without full evaluation, and deploying evolutionary algorithms to navigate the combinatorial optimization space efficiently. This reduces the computational burden of testing every possible client combination while maintaining solution quality. The metric function incorporating communication latency and energy consumption reflects real-world constraints that academic research often overlooks.

For the distributed systems and AI infrastructure sectors, this work has meaningful implications. Federated learning deployments increasingly power on-device ML applications, IoT networks, and privacy-preserving enterprise systems. Optimizing client selection directly impacts operational costs, training time, and model reliability—metrics that enterprise clients actively monitor. The demonstrated improvements across multiple datasets (MNIST, CIFAR10, CINIC10, TinyImageNet) suggest the approach generalizes across problem domains.

The energy efficiency gains are particularly relevant as sustainability concerns drive hardware procurement decisions. Organizations operating large federated networks could reduce computational waste and associated carbon footprint through intelligent client selection. Future development should explore real-world deployment scenarios and integration with production federated learning platforms to validate practical impact.

• →EvoCSFL uses surrogate models and evolutionary algorithms to optimize federated learning client selection beyond random approaches.
• →The framework balances model performance, communication latency, and energy consumption in a single optimization metric.
• →Experiments demonstrate faster convergence and lower energy consumption compared to existing selection methods.
• →The approach addresses real-world constraints often ignored in academic federated learning research.
• →Surrogate-assisted optimization reduces computational overhead of evaluating client subset combinations.