Client Selection

Client selection in federated learning (FL) aims to optimize the participation of distributed clients in model training, balancing model accuracy, communication efficiency, and fairness. Current research focuses on developing sophisticated algorithms, often employing techniques like submodular maximization, bilevel optimization, and generative models, to select clients based on various criteria including data quality, resource availability, and privacy constraints. These advancements are crucial for improving the efficiency and robustness of FL, enabling its wider adoption in privacy-sensitive applications like healthcare and finance.