Participant Selection

Participant selection in distributed machine learning, particularly federated learning (FL), aims to optimize model training by strategically choosing which data sources contribute in each iteration. Current research focuses on developing algorithms that balance factors like data quality, computational resources, and communication efficiency, often employing techniques such as Bayesian nonparametric methods, auction-based approaches, and determinantal point processes to select participants effectively. These advancements are crucial for improving the speed, accuracy, and cost-effectiveness of FL, with implications for various applications ranging from mobile device training to large-scale clinical studies.