Federated learning addresses data security concerns by ensuring that sensitive data remains on the device where it is generated and is never sent to a central server. In traditional machine learning, data is aggregated in a central location, which poses a significant privacy risk. In contrast, federated learning allows models to be trained across multiple devices while only sharing updates based on local training. This means that personal data, like medical records or user preferences, stays local, significantly reducing the risk of data breaches.
An important aspect of federated learning is its approach to model updates. Instead of sending raw data to a central server, each device trains a model on its local data and calculates model updates. These updates are then sent to a central server, where they are aggregated to improve a global model. For example, if a company is developing a predictive text application on users' mobile devices, each device learns from its user's typing habits without ever transmitting the actual text typed. This process not only keeps sensitive data secure but also minimizes the chances of unauthorized access.
Furthermore, federated learning can incorporate various privacy-preserving techniques like differential privacy and secure multiparty computation. Differential privacy adds noise to the model updates, ensuring that no individual user's data can be inferred from the aggregated information. Secure multiparty computation allows multiple parties to collaborate on computations without revealing their individual data. These methods enhance data security and privacy further, making federated learning a powerful approach for handling sensitive information across various applications, from healthcare to financial services.