# Backdoor attacks and defenses in feature-partitioned collaborative learning

arXiv: https://arxiv.org/abs/2007.03608 · HTML: https://mlsanity.com/paper/2007.03608/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 30 of 30)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions](https://mlsanity.com/paper/2303.02213/) (2023)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning](https://mlsanity.com/paper/2112.05409/) (2021)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM](https://mlsanity.com/paper/2207.10226/) (2022)
- [ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems](https://mlsanity.com/paper/2201.02775/) (2022)
- [Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm](https://mlsanity.com/paper/2203.10329/) (2022)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [VFLAIR: A Research Library and Benchmark for Vertical Federated Learning](https://mlsanity.com/paper/2310.09827/) (2023)
- [Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond](https://mlsanity.com/paper/2207.09087/) (2022)
- [Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration](https://mlsanity.com/paper/2304.11432/) (2023)
- [Practical and General Backdoor Attacks against Vertical Federated Learning](https://mlsanity.com/paper/2306.10746/) (2023)
- [Hijack Vertical Federated Learning Models As One Party](https://mlsanity.com/paper/2212.00322/) (2022)
- [Mutual Information Regularization for Vertical Federated Learning](https://mlsanity.com/paper/2301.01142/) (2023)
- [Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning](https://mlsanity.com/paper/2501.09320/) (2025)
- [Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization](https://mlsanity.com/paper/2306.16077/) (2023)
- [Let's Focus: Focused Backdoor Attack against Federated Transfer Learning](https://mlsanity.com/paper/2404.19420/) (2024)
- [Attacks on fairness in Federated Learning](https://mlsanity.com/paper/2311.12715/) (2023)
- [Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization](https://mlsanity.com/paper/2502.20565/) (2025)
- [Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning](https://mlsanity.com/paper/2411.02809/) (2024)
- [An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers](https://mlsanity.com/paper/2412.06149/) (2024)
- [Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning](https://mlsanity.com/paper/2507.07259/) (2025)
- [VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning](https://mlsanity.com/paper/2507.14625/) (2025)
- [A Privacy by Design Framework for Large Language Model-Based Applications for Children](https://mlsanity.com/paper/2602.17418/) (2026)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

Since there are multiple parties in collaborative learning, malicious parties might manipulate the learning process for their own purposes through backdoor attacks. However, most of existing works only consider the federated learning scenario where data are partitioned by samples. The feature-partitioned learning can be another important scenario since in many real world applications, features are often distributed across different parties. Attacks and defenses in such scenario are especially challenging when the attackers have no labels and the defenders are not able to access the data and model parameters of other participants. In this paper, we show that even parties with no access to labels can successfully inject backdoor attacks, achieving high accuracy on both main and backdoor tasks. Next, we introduce several defense techniques, demonstrating that the backdoor can be successfully blocked by a combination of these techniques without hurting main task accuracy. To the best of our knowledge, this is the first systematical study to deal with backdoor attacks in the feature-partitioned collaborative learning framework.