Part 7/9:
The federated learning process begins with an initial shared model, distributed to all collaborators. Each institution trains this model on its local dataset—whether it involves human tissues or mouse data—without sharing raw data externally. The models are then sent back to a central aggregator, which combines or averages the updates to create a refined, more accurate model.
This process iterates across multiple rounds, often taking from fifty to hundreds, until the model reaches an impressive 95% to 99% accuracy. This iterative approach ensures that insights are gleaned from diverse and distributed datasets while maintaining privacy and security.