Assess the learner's ability to design a privacy-preserving federated learning system for heterogeneous mobile devices, covering differential privacy mechanisms and noise calibration, secure aggregation protocols and threat models, resource- and connectivity-aware client selection strategies, trade-offs between privacy, utility, communication and computation, handling stragglers and device heterogeneity, and how to evaluate and validate the system via metrics, simulations, and deployment considerations.