Technology
Federated Learning Explained
Discover Federated Learning, a privacy-first AI technique that trains models on decentralized data without ever moving it. Learn how it works.
What is it?
Federated Learning is a machine learning technique that trains algorithms across multiple decentralized devices holding local data, without exchanging the data itself. Instead of pooling data centrally, a shared model is sent to individual devices. Each device trains the model on its local data, and only the resulting model updates are sent back to a central server. These updates are then aggregated to improve the shared global model. This approach is often described as "bringing the model to the data," rather than bringing the data to the model.
Why is it trending?
The rise of Federated Learning is linked to the growing global emphasis on data privacy and security regulations like GDPR. Industries handling sensitive information, such as healthcare and finance, use it to build powerful AI models without sharing raw data. This method is also efficient, avoiding the massive costs and bandwidth required to transfer large datasets. It enables collaboration among organizations that would otherwise be unable to share data, unlocking new possibilities for AI development while respecting user privacy and reducing data transfer needs.
How does it affect people?
Federated Learning enhances privacy while improving personalized services. For instance, your smartphone's predictive keyboard improves by learning from your typing patterns without your messages ever leaving your device. In healthcare, it allows hospitals to collaboratively train a diagnostic AI on patient data from different locations to detect diseases more accurately, all without compromising confidentiality. This means better technology and more advanced medical research, all while keeping personal data secure on local devices, giving users more control over their information.