In federated learning, client devices refer to the individual devices or systems that participate in the training of machine learning models without directly sharing their data. Instead of centralizing data in a cloud server, federated learning allows client devices, such as smartphones, tablets, or IoT devices, to perform computations locally on their own datasets. This approach helps maintain user privacy and security since sensitive data does not leave the device. After training on local data, the devices send only the model updates—like weights and gradients—back to a central server, which aggregates these updates to improve the overall model.
The architecture of federated learning generally involves multiple client devices working in parallel. For instance, imagine a scenario in which several smartphones are being used to improve a predictive text feature in a messaging app. Each phone collects data from user interactions and uses it to train a local model. Once the training phase is complete, the devices send their updates to the central server, which synthesizes the information from all devices. This aggregated model is then sent back to the client devices, providing them with a version of the model that is enhanced based on collective user behavior without exposing any individual data.
Moreover, the use of client devices allows federated learning to leverage the diverse data that different users possess, which is essential for creating robust and accurate models. For example, a fitness app that tracks user activity can benefit from analyzing data across various users' exercise patterns while preserving individual privacy. By adopting federated learning, developers can develop models that are not only more accurate but also more respectful of users' privacy, fostering trust and ensuring compliance with regulations like GDPR. This system creates a collaborative learning environment, where the benefits of multiple devices' learning contribute to a stronger overall model.