Assess graduate students' ability to analyze trade-offs between privacy, utility, and fairness in federated learning for healthcare data, including evaluation of privacy-preserving methods (e.g., differential privacy, secure aggregation), their quantitative impact on model performance, fairness metrics across patient subgroups, experimental design considerations, and regulatory/ethical constraints; require proposals for strategies to balance competing objectives and justify choices with evidence-based reasoning and suitable evaluation plans.