  Machine Learning models require a vast amount of data for accurate training.
In reality, most data is scattered across different organizations and cannot be
easily integrated under many legal and practical constraints. Federated
Transfer Learning (FTL) was introduced in [1] to improve statistical models
under a data federation that allow knowledge to be shared without compromising
user privacy, and enable complementary knowledge to be transferred in the
network. As a result, a target-domain party can build more flexible and
powerful models by leveraging rich labels from a source-domain party. However,
the excessive computational overhead of the security protocol involved in this
model rendered it impractical. In this work, we aim towards enhancing the
efficiency and security of existing models for practical collaborative training
under a data federation by incorporating Secret Sharing (SS). In literature,
only the semi-honest model for Federated Transfer Learning has been considered.
In this paper, we improve upon the previous solution, and also allow malicious
players who can arbitrarily deviate from the protocol in our FTL model. This is
much stronger than the semi-honest model where we assume that parties follow
the protocol precisely. We do so using the one of the practical MPC protocol
called SPDZ, thus our model can be efficiently extended to any number of
parties even in the case of a dishonest majority. In addition, the models
evaluated in our setting significantly outperform the previous work, in terms
of both runtime and communication cost. A single iteration in our model
executes in 0.8 seconds for the semi-honest case and 1.4 seconds for the
malicious case for 500 samples, as compared to 35 seconds taken by the previous
implementation.
