Secure Federated Transfer Learning
This addresses privacy and data integration challenges for organizations in machine learning, though it appears incremental as it builds on existing federated and transfer learning concepts.
The paper tackles the problem of scattered data across organizations by introducing federated transfer learning (FTL), a framework that enables knowledge sharing without compromising privacy, achieving the same accuracy as non-privacy-preserving approaches.
Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary 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. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.