Part 5/10:
To circumvent data sharing restrictions, researchers are turning to federated learning, a revolutionary approach introduced by Intel.
Unlike traditional machine learning, which involves bringing all data to a central server for training, federated learning trains models locally on data within each institution. Instead of transferring sensitive data, only model updates are shared and aggregated, thus preserving privacy while harnessing a diverse and expansive data pool.
This framework allows for access to data that would otherwise be off-limits, opening new vistas for research and development especially in sensitive fields like healthcare.