mlsanityData to 7 Oct 2026

FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof

arXiv 2305.04507 ↗First posted 8 May 2023cs.CR cs.AIcited by 9

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Abstract (arXiv)

Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models from being plagiarized or misused, therefore, motivates us to propose a provable secure model ownership verification scheme using zero-knowledge proof, named FedZKP. It is shown that the FedZKP scheme without disclosing credentials is guaranteed to defeat a variety of existing and potential attacks. Both theoretical analysis and empirical studies demonstrate the security of FedZKP in the sense that the probability for attackers to breach the proposed FedZKP is negligible. Moreover, extensive experimental results confirm the fidelity and robustness of our scheme.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

19 of 50 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,041
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  6. 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
  7. 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
  8. 2013One Billion Word Benchmark for Measuring Progress in Statistical Language Modelingcited by 302
  9. 2017Embedding Watermarks into Deep Neural Networkscited by 227
  10. 2019Deep Neural Network Fingerprinting by Conferrable Adversarial Examplescited by 90
  11. 2019[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attackscited by 82
  12. 2021Proof-of-Learning: Definitions and Practicecited by 81
7 more
  1. 2019IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundarycited by 68
  2. 2016Compression of Neural Machine Translation Models via Pruningcited by 43
  3. 2020Passport-aware Normalization for Deep Model Protectioncited by 42
  4. 2020WAFFLE: Watermarking in Federated Learningcited by 34
  5. 2021ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detectioncited by 33
  6. 2021FedIPR: Ownership Verification for Federated Deep Neural Network Modelscited by 33
  7. 2018DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networkscited by 23

Built on by

Cited by year (2026 to 7 Oct)

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

9 papers.

  1. 2023Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Surveycited by 18
  2. 2025Do we really have to filter out random noise in pre-training data for language models?cited by 11
  3. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 6
  4. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  5. 2024RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protectioncited by 3
  6. 2023zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proofcited by 1
  7. 2025FedPoP: Federated Learning Meets Proof of Participationcited by 1
  8. 2026Collaborative Threshold Watermarkingcited by 0
  9. 2026ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoringcited by 0