Explore hierarchical federated learning using [[Substrate]]'s networking capabilities, where local models are aggregated at regional nodes before global aggregation, reducing communication overhead. This hierarchical structure minimizes the amount of data transferred to the global aggregator, making federated learning more efficient for large networks, such as in a global initiative involving thousands of participants training a shared AI model coordinated through Substrate's [[Proof Pod Function and Architecture|off-chain workers]]. See also: [[ZKP/Research/Advanced Federated Learning/Collusion-Resistant Mechanisms|Collusion-Resistant Mechanisms]]