Secure and Efficient Federated Transfer Learning
This work addresses efficiency and security challenges for organizations needing collaborative training under data privacy constraints, though it is incremental as it builds on existing FTL models.
The paper tackles the problem of excessive computational overhead in Federated Transfer Learning (FTL) by incorporating Secret Sharing and SPDZ protocol, resulting in a model that executes in 0.8 seconds for semi-honest and 1.4 seconds for malicious cases for 500 samples, compared to 35 seconds in previous work.
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.