Intellectual Property (IP) Protection for Deep Learning and Federated Learning Models

This talk focuses on end-to-end protection of the present and emerging Deep Learning (DL) and Federated Learning (FL) models. On the one hand, DL and FL models are usually trained by allocating significant computational resources to process massive training data. The built models are therefore considered as the owner's IP and need to be protected. On the other hand, malicious attackers may take advantage of the models for illegal usages. IP protection needs to be considered during the design and training of the DL models before the owners make their models publicly available. The tremendous parameter space of DL models allows them to learn hidden features automatically.