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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
- Data Stays Local
- Unlike traditional methods where data is sent to a central system for training, Federated Learning keeps everything on your device.
- Secure and Private
- Since raw data isn’t shared, it reduces the risk of privacy violations.
- Collaboration Without Sharing Data
- Devices work together to improve a shared model without exposing personal data.
How Federated Learning Works
- Training on Your Device
- Each device uses its local data to train a small part of the model.
- Sharing Model Updates
- Instead of sending your data, the device shares only the updates from the training process.
- Global Model Improvement
- These updates are combined on a central server to create a better global model.
- Updates Sent Back to Devices
- The improved global model is sent to devices, making them smarter over time.
Where Federated Learning is Used
- Healthcare
- Hospitals can create better medical AI systems by sharing insights without exposing patient information.
- Banking
- Banks can detect fraud by learning from data across branches without sharing sensitive details.
- Smartphones and IoT Devices
- Features like predictive typing or smart assistants get better without compromising user privacy.