Camouflage Learning
Stephan Sigg, Le Ngu Nguyen, Jing Ma · 2021
Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.