TY  - GEN 
AU  - Fei, Qinjun
AU  - Rodríguez Barroso, Nuria
AU  - Luzón García, María Victoria
AU  - Zhang, Zhongliang
AU  - Herrera Triguero, Francisco
PY  - 2026
UR  - https://hdl.handle.net/10481/107151
AB  - In cross-silo Federated Learning (FL), client selection is critical to ensure high model performance, yet it remains challenging due to data quality decompensation, budget constraints, and incentive compatibility. As training progresses, these factors...
LA  - eng
PB  - Elsevier
KW  - Federated learning
KW  - Reputation-based client selection
KW  - Data quality decompensation
TI  - Addressing data quality decompensation in federated learning via dynamic client selection
DO  - 10.1016/j.future.2025.108138
ER  -