| Description
| This course focuses on algorithmic design and practical implementation of Federated Learning in smart healthcare applications. It addresses distributed model training, aggregation strategies, communication efficiency, and system performance evaluation under non-centralized data settings. Emphasis is placed on privacy-preserving mechanisms such as differential privacy, secure aggregation, and robust model updating in cross-institutional medical AI systems. Students will engage in hands-on engineering projects to develop deployable federated healthcare AI frameworks. The course centers on algorithms, experimental validation, and system-level implementation rather than data structure theory, aiming to cultivate the ability to design efficient and privacy-aware distributed medical AI systems.
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