Hao-Xuan Geng

History-Aware Multi-Objective Client Selection for Federated Learning under Statistical and System Heterogeneity

Federated learning reduces raw-data movement, but partial participation under statistical and system heterogeneity makes the selected client cohort a major source of optimisation bias and delay. This study proposes a history-aware multi-objective selector that combines opt-in data-quality metadata, exponentially smooth...