# Secure Federated Transfer Learning

## TL;DR

This paper introduces a secure federated transfer learning framework that enables multiple organizations to collaboratively improve machine learning models while preserving privacy, with minimal modifications and comparable accuracy to traditional methods.

## Contribution

The paper proposes a novel federated transfer learning framework that enhances model performance across organizations without compromising privacy, including a secure validation method.

## Key findings

- Achieves comparable accuracy to non-privacy-preserving models
- Requires minimal modifications to existing models
- Effective across various secure multi-party ML tasks

## Abstract

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.