Federated learning is a type of machine learning technique that allows models to be trained across multiple decentralized devices or servers while keeping the training data local. This means that the data never leaves its original device, making it a great choice for scenarios where privacy and security are important. The primary use cases of federated learning include areas such as healthcare, mobile device personalization, and smart home technologies.
In healthcare, federated learning can enable hospitals and medical research facilities to collaborate on developing predictive models without sharing sensitive patient data. For example, different hospitals can train a shared model to predict patient outcomes based on local data, which helps them adhere to data privacy regulations like HIPAA. This way, they can improve model accuracy using diverse datasets while keeping each institution's information confidential.
Another notable use case is in mobile device personalization. For instance, smartphone companies can use federated learning to improve predictive text or voice recognition systems based on user interactions without collecting individual typing or voice data. Instead of sending sensitive user data to the cloud for processing, the model learns from local data on each device and only sends model updates back to central servers. This approach enhances user experience by providing personalized features while maintaining user privacy, ultimately fostering trust in the technology.