2 papers
cs.AI2025
CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters
Xiang Liu, Hau Chan, Minming Li +3
Federated learning (FL) is a promising approach that allows requesters (\eg, servers) to obtain local training models from workers (e.g., clients). Since workers are typically unwi…
cs.HC2024
Enhancing the Interpretability of SHAP Values Using Large Language Models
Xianlong Zeng, Kewen Zhu
Model interpretability is crucial for understanding and trusting the decisions made by complex machine learning models, such as those built with XGBoost. SHAP (SHapley Additive exP…