Federated Learning

Federated Learning is a type of machine learning where devices like smartphones or IoT gadgets learn from data without sending it to a central server. This means your private information stays on your device while the system improves its performance through shared learning.

Why Federated Learning is Different

1. Data Stays Local
   • Unlike traditional methods where data is sent to a central system for training, Federated Learning keeps everything on your device.
2. Secure and Private
   • Since raw data isn’t shared, it reduces the risk of privacy violations.
3. Collaboration Without Sharing Data
   • Devices work together to improve a shared model without exposing personal data.

How Federated Learning Works

1. Training on Your Device
   • Each device uses its local data to train a small part of the model.
2. Sharing Model Updates
   • Instead of sending your data, the device shares only the updates from the training process.
3. Global Model Improvement
   • These updates are combined on a central server to create a better global model.
4. Updates Sent Back to Devices
   • The improved global model is sent to devices, making them smarter over time.

Where Federated Learning is Used

1. Healthcare
   • Hospitals can create better medical AI systems by sharing insights without exposing patient information.
2. Banking
   • Banks can detect fraud by learning from data across branches without sharing sensitive details.
3. Smartphones and IoT Devices
   • Features like predictive typing or smart assistants get better without compromising user privacy.