# Secure and Efficient Federated Transfer Learning

arXiv: https://arxiv.org/abs/1910.13271 · HTML: https://mlsanity.com/paper/1910.13271/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning](https://mlsanity.com/paper/2106.06312/) (2021)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Personalised Federated Learning On Heterogeneous Feature Spaces](https://mlsanity.com/paper/2301.11447/) (2023)
- [Multiplayer Federated Learning: Reaching Equilibrium with Less Communication](https://mlsanity.com/paper/2501.08263/) (2025)
- [Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms](https://mlsanity.com/paper/2207.02337/) (2022)
- [Federated Transfer Learning with Multimodal Data](https://mlsanity.com/paper/2209.03137/) (2022)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation](https://mlsanity.com/paper/2312.00102/) (2023)
- [Vertical Federated Learning Hybrid Local Pre-training](https://mlsanity.com/paper/2405.11884/) (2024)
- [VerifiableFL: Verifiable Claims for Federated Learning using Exclaves](https://mlsanity.com/paper/2412.10537/) (2024)
- [Framework Construction of an Adversarial Federated Transfer Learning Classifier](https://mlsanity.com/paper/2211.04734/) (2022)
- [Survey of Privacy Threats and Countermeasures in Federated Learning](https://mlsanity.com/paper/2402.00342/) (2024)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)
- [ION-C: Integration of Overlapping Networks via Constraints](https://mlsanity.com/paper/2411.04243/) (2024)

## Abstract (arXiv)

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.