Part 5/7:
Unlike traditional methods that require consolidating all data in a central database—a process often hindered by privacy concerns, logistical difficulties, and data volume—federated learning allows models to be trained across multiple data sources without transferring sensitive information. Instead, the model itself moves between data centers, learning from distributed datasets while keeping the raw data securely localized. This approach enhances data diversity, enabling more accurate and comprehensive models.