User Selection

User selection in machine learning, particularly within federated learning frameworks, aims to optimize the participation of users to improve model training efficiency and performance. Current research focuses on developing dynamic selection strategies, often employing probabilistic or reinforcement learning approaches, to account for fluctuating network conditions, user data availability, and energy constraints. These methods address challenges like minimizing communication overhead, ensuring fairness, and maximizing model accuracy, impacting the design of efficient and robust decentralized learning systems. The resulting advancements have implications for various applications, including resource-constrained wireless networks and personalized marketing strategies.