Federated learning ensures that data remains on client devices by decentralizing the training process and distributing the model updates instead of the actual data. In a traditional training setup, training data is collected and sent to a central server where the model training occurs. In contrast, in federated learning, the client devices—such as smartphones or IoT devices—hold the data. The model is trained locally on each device using the data stored there, which means the actual data never leaves the device.
To implement federated learning, each device first downloads a global model from the server. The device then trains this model using its local data, making adjustments based on the unique dataset it has. Once the training is complete, only the updates or changes to the model's parameters are sent back to the central server, not the data itself. This process is often referred to as "training on-device." For example, in a mobile keyboard application, the app can learn from users’ typing patterns to improve predictions without ever sharing sensitive text data with the server.
Additionally, federated learning incorporates techniques to further protect data privacy. One common method is the use of differential privacy, which adds noise to the model updates before they are sent to the server, ensuring individual data points cannot be reconstructed. In this way, developers can build powerful machine learning models while maintaining user confidentiality and complying with data privacy regulations, such as GDPR. This approach enables collaboration on model development without compromising the security of the users' data.