  The success of machine learning algorithms often relies on a large amount of
high-quality data to train well-performed models. However, data is a valuable
resource and are always held by different parties in reality. An effective
solution to such a data isolation problem is to employ federated learning,
which allows multiple parties to collaboratively train a model. In this paper,
we propose a Secure version of the widely used Maximum Mean Discrepancy (SMMD)
based on homomorphic encryption to enable effective knowledge transfer under
the data federation setting without compromising the data privacy. The proposed
SMMD is able to avoid the potential information leakage in transfer learning
when aligning the source and target data distribution. As a result, both the
source domain and target domain can fully utilize their data to build more
scalable models. Experimental results demonstrate that our proposed SMMD is
secure and effective.
