# Secure Federated Transfer Learning

arXiv: https://arxiv.org/abs/1812.03337 · HTML: https://mlsanity.com/paper/1812.03337/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 13 of 13)

- [Federated Multi-Target Domain Adaptation](https://mlsanity.com/paper/2108.07792/) (2021)
- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning](https://mlsanity.com/paper/2208.08934/) (2022)
- [Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning](https://mlsanity.com/paper/2112.05409/) (2021)
- [Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX](https://mlsanity.com/paper/2105.01281/) (2021)
- [Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data](https://mlsanity.com/paper/2203.01752/) (2022)
- [Production federated keyword spotting via distillation, filtering, and joint federated-centralized training](https://mlsanity.com/paper/2204.06322/) (2022)
- [Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health](https://mlsanity.com/paper/2108.10761/) (2021)
- [VFLAIR: A Research Library and Benchmark for Vertical Federated Learning](https://mlsanity.com/paper/2310.09827/) (2023)
- [Mutual Information Regularization for Vertical Federated Learning](https://mlsanity.com/paper/2301.01142/) (2023)
- [Multi-Party Dual Learning](https://mlsanity.com/paper/2104.06677/) (2021)
- [Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks](https://mlsanity.com/paper/2112.00988/) (2021)
- [Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy](https://mlsanity.com/paper/2204.13697/) (2022)

## Abstract (arXiv)

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.