Federated learning can help reduce the risk of data breaches, but it does not completely eliminate them. In federated learning, the model is trained across multiple devices without requiring raw data to be sent to a central server. Instead, each device processes its own local data and sends only the model updates back to the server. This approach minimizes the exposure of sensitive data during the training process, making it harder for attackers to access a centralized dataset that might contain personal information.
For example, consider a healthcare application where patient data is highly sensitive. Instead of collecting and storing patient records on a central server, federated learning allows hospitals and clinics to collaborate on improving a predictive model without sharing the actual patient data. Each institution trains the model based on their own data and just sends the updates (like gradients) to the central server. By doing this, even if the central server is compromised, attackers would only gain access to model updates that do not contain any patient-specific information, significantly limiting the potential for data breaches.
However, while federated learning enhances privacy, it's not a silver bullet. There are still vulnerabilities to consider, such as risks from model inference attacks where attackers might deduce information about the local data based on the shared model updates. To further safeguard against these risks, additional techniques like differential privacy can be applied to obscure the data even more. Thus, while federated learning reduces the likelihood and impact of data breaches, developers should adopt a multi-layered approach to security that incorporates various privacy-preserving techniques.