Federated Learning (FL) enables collaborative model training across decentralized data silos while preserving data privacy. However, client selection strategies in conventional FL processes typically rely on single-dimensional evaluation metrics, which fail to capture data diversity and overlook the dynamic nature of client contributions, particularly in domains characterized by sparse and heterogeneous data, such as healthcare and drug discovery. These limitations ultimately hinder the global model's generalization ability and reduce training efficiency. To address these challenges, this paper proposes an adaptive FL framework that employs a hybrid contribution evaluation mechanism as the core principle for client selection and resource management. The proposed approach quantifies each client's effectiveness by integrating two complementary dimensions: (i) a performance-based evaluation that measures the immediate impact of a client's update on the global optimization trajectory, and (ii) a coverage-based evaluation that estimates data diversity in the latent embedding space without exposing raw data. By combining these two criteria, the hybrid mechanism ensures that highly contributive clients are preferentially selected while preventing the permanent exclusion of any participant, thereby maintaining a balanced trade-off between efficiency and fairness. Experimental results demonstrate that the proposed framework outperforms existing FL baselines in terms of training efficiency, data utilization, and fairness.
Jeong et al. (Wed,) studied this question.