mlsanityData to 7 Oct 2026

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

arXiv 2211.07138 ↗First posted 14 Nov 2022cs.CR cs.AI cs.LGcited by 14

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Abstract (arXiv)

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To our best knowledge, it is the first scheme to embed the watermark to models under the Secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 25 of its 51 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

25 of 51 references.

References · 25

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
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  4. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  5. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  6. 2018How To Backdoor Federated Learningcited by 745
  7. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  10. 2019Federated Machine Learning: Concept and Applicationscited by 409
  11. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  12. 2020Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkscited by 341
13 more
  1. 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
  2. 2017Embedding Watermarks into Deep Neural Networkscited by 227
  3. 2021Dataset Inference: Ownership Resolution in Machine Learningcited by 115
  4. 2017Adversarial Frontier Stitching for Remote Neural Network Watermarkingcited by 110
  5. 2019DAWN: Dynamic Adversarial Watermarking of Neural Networkscited by 94
  6. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  7. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 78
  8. 2019How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNNcited by 58
  9. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  10. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 46
  11. 2020WAFFLE: Watermarking in Federated Learningcited by 34
  12. 2020Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Modelscited by 18
  13. 2021Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Imagescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 6; 2026: 3.

14 papers.

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  1. 2024Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attributioncited by 35
  2. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  3. 2025FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprintcited by 8
  4. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 6
  5. 2024Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directionscited by 4
  6. 2025FedPoP: Federated Learning Meets Proof of Participationcited by 1
  7. 2023FedReverse: Multiparty Reversible Deep Neural Network Watermarkingcited by 0
  8. 2024Watermarking Recommender Systemscited by 0
  9. 2025Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networkscited by 0
  10. 2025Robust Client-Server Watermarking for Split Federated Learningcited by 0
  11. 2025Server-Enforced Watermarking in U-Shaped Split Federated Learningcited by 0
  12. 2026Collaborative Threshold Watermarkingcited by 0
2 more
  1. 2026FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMscited by 0
  2. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0