Assess the learner’s ability to design a privacy-preserving, fault-tolerant federated learning architecture for heterogeneous edge devices that integrates secure aggregation, differential privacy, model compression, and adaptive personalization. Expect justification of algorithmic and protocol choices (cryptographic primitives, DP mechanism and budgeting, compression/quantization methods), strategies for handling device heterogeneity and failures, communication/computation trade-offs, evaluation metrics, and a deployment or simulation plan demonstrating scalability and privacy-utility trade-offs.