3.2.3 Privacy-Preserving Collaborative Training

To ensure the privacy of user data during the training process, AISim integrates a federated learning framework, supporting collaborative training between multiple devices. Through federated learning, data on different nodes can share computational results without exposing actual data. Additionally, AISim provides an encrypted model update mechanism to ensure that all model training and data analysis remain encrypted during cross-node collaboration, protecting the privacy of users and devices.