Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation

Privacy-preserving machine learning is becoming increasingly important in today's world where data privacy is a major concern. Federated learning and secure aggregation are two techniques that can be used to achieve privacy-preserving machine learning.

Federated learning is a technique where the machine learning model is trained on data that is distributed across multiple devices or servers. In this technique, the model is sent to the devices or servers, and the devices or servers perform the training locally on their own data. The trained model updates are then sent back to a central server, where they are aggregated to create a new version of the model. The key advantage of federated learning is that the data remains on the devices or servers, which helps to protect the privacy of the data.

Secure aggregation is a technique that can be used to protect the privacy of the model updates that are sent to the central server. In this technique, the updates are encrypted before they are sent to the central server. The central server then performs the aggregation operation on the encrypted updates, and the result is sent back to the devices or servers. The devices or servers can then decrypt the result to obtain the updated model.

By combining federated learning and secure aggregation, it is possible to achieve privacy-preserving machine learning. This approach allows for the training of machine learning models on sensitive data while protecting the privacy of the data and the model updates.