Secure Federated Transfer Learning
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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.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Dec 2018.
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Citing papers per year: 2021: 7; 2022: 4; 2023: 2.
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- 2021Federated Multi-Target Domain Adaptationcited by 44
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
- 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
- 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
- 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGXcited by 14
- 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
- 2022Production federated keyword spotting via distillation, filtering, and joint federated-centralized trainingcited by 9
- 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Healthcited by 8
- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
- 2021Multi-Party Dual Learningcited by 3
- 2023Mutual Information Regularization for Vertical Federated Learningcited by 3
- 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networkscited by 2
- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learning15 Oct 2023
- 2023Mutual Information Regularization for Vertical Federated Learning1 Jan 2023
- 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning18 Aug 2022
- 2022Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy22 Apr 2022
- 2022Production federated keyword spotting via distillation, filtering, and joint federated-centralized training11 Apr 2022
- 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data3 Mar 2022
- 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning10 Dec 2021
- 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks2 Dec 2021
- 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health24 Aug 2021
- 2021Federated Multi-Target Domain Adaptation17 Aug 2021
- 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX4 May 2021
- 2021Multi-Party Dual Learning14 Apr 2021
- 2021Federated Multi-Target Domain Adaptationcited by 44
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
- 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
- 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
- 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGXcited by 14
- 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
- 2022Production federated keyword spotting via distillation, filtering, and joint federated-centralized trainingcited by 9
- 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Healthcited by 8
- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
- 2021Multi-Party Dual Learningcited by 3
- 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networkscited by 2
- 2022Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacycited by 1
- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learning15 Oct 2023
- 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning18 Aug 2022
- 2022Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy22 Apr 2022
- 2022Production federated keyword spotting via distillation, filtering, and joint federated-centralized training11 Apr 2022
- 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data3 Mar 2022
- 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning10 Dec 2021
- 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks2 Dec 2021
- 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health24 Aug 2021
- 2021Federated Multi-Target Domain Adaptation17 Aug 2021
- 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX4 May 2021
- 2021Multi-Party Dual Learning14 Apr 2021
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation4 Jan 2021