Private Federated Learning with Domain Adaptation

We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model. In FL, multiple parties wish to perform essentially the same task using ML, with a model structure that is agreed upon in advance. Although the initial focus of FL has been on targeting millions of mobile devices (5), the benefits of its architecture are beneficial even for enterprise settings: the number of users of an ML service may be much smaller, but privacy concerns are paramount. Each user wants the best possible classifier for their individual use, but has a limited budget for labeling their own data.