Which technology allows models to be trained on decentralized devices while preserving data privacy?

Federated Learning is the technology that enables models to be trained on decentralized devices while ensuring data privacy. This approach allows individual devices, such as smartphones or IoT devices, to learn from local data without needing to share that data with a central server. Instead of sending raw data, only model updates or gradients are communicated back to the server. This way, sensitive information remains on the user’s device, preventing exposure during the training process.

The decentralized nature of Federated Learning is particularly advantageous for sensitive data scenarios where privacy is paramount; for instance, in healthcare or financial applications, where regulations such as HIPAA or GDPR require stringent data protection measures. The method enhances collaboration among devices without compromising individual privacy, making it a powerful tool in the era of data-driven applications.