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.

本文介绍了一种新的技术和框架，称为联邦迁移学习（FTL），它可以在数据联邦下提高统计模型的性能。该联邦允许共享知识而不会危及用户隐私，并可在网络中传输补充知识，从而使目标域方能够利用源域方的丰富标签构建更加灵活和强大的模型。同时，为了在联邦下保护 FTL 的性能，还提出了一种安全的跨验证方法。该框架需要对现有模型结构进行最小的修改，并提供与非隐私保护方法同样水平的准确性。该框架非常灵活，可有效适应各种安全多方机器学习任务。