mlsanityData to 14 Oct 2026

Backdoor attacks and defenses in feature-partitioned collaborative learning

arXiv 2007.03608 ↗First posted 7 Jul 2020cs.LG stat.MLcited by 30

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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.

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

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Citing papers per year: 2021: 1; 2022: 7; 2023: 12; 2024: 4; 2025: 4; 2026: 2.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  3. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  4. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  5. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  6. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 20
  7. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  8. 2021Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learningcited by 16
  9. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  10. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  11. 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 9
  12. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
18 more
  1. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  2. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  3. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7
  4. 2023Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaborationcited by 6
  5. 2023Practical and General Backdoor Attacks against Vertical Federated Learningcited by 6
  6. 2022Hijack Vertical Federated Learning Models As One Partycited by 5
  7. 2023Mutual Information Regularization for Vertical Federated Learningcited by 4
  8. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learningcited by 3
  9. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  10. 2024Let's Focus: Focused Backdoor Attack against Federated Transfer Learningcited by 2
  11. 2023Attacks on fairness in Federated Learningcited by 1
  12. 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimizationcited by 1
  13. 2024Query-Efficient Adversarial Attack Against Vertical Federated Graph Learningcited by 0
  14. 2024An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformerscited by 0
  15. 2025Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learningcited by 0
  16. 2025VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learningcited by 0
  17. 2026A Privacy by Design Framework for Large Language Model-Based Applications for Childrencited by 0
  18. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0