Federated Learning for Multi-Center Medical Image Diagnostics

Explain the core concepts and workflow of Federated Learning in healthcare AI.

Design a basic multi-center federated training architecture for medical imaging.

Identify and address challenges such as data heterogeneity, bias, and communication efficiency.

Apply privacy-preserving techniques including secure aggregation and differential privacy.

Evaluate the performance and generalizability of federated models for MRI, CT, and X-ray diagnostics.

Outline strategies for deploying compliance-ready, scalable AI systems in clinical environments.