mlsanityData to 7 Oct 2026

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

arXiv 2211.07160 ↗First posted 14 Nov 2022cs.CR cs.AI cs.LGcited by 25

On the map: LLM safety and security → Federated unlearning and poisoning

Abstract (arXiv)

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the malicious client, compromising the intellectual property rights of the FL group. To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants. In this paper, we present FedTracker, the first FL model protection framework that provides both ownership verification and traceability. FedTracker adopts a bi-level protection scheme consisting of global watermark mechanism and local fingerprint mechanism. The former authenticates the ownership of the global model, while the latter identifies which client the model is derived from. FedTracker leverages Continual Learning (CL) principles to embed the watermark in a way that preserves the utility of the FL model on both primitive task and watermark task. FedTracker also devises a novel metric to better discriminate different fingerprints. Experimental results show FedTracker is effective in ownership verification, traceability, and maintains good fidelity and robustness against various watermark removal attacks.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. Builds on 33 of its 66 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

33 of 66 references.

References · 33

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21 more
  1. 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 446
  2. 2020Backdoor Learning: A Surveycited by 412
  3. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  4. 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 11; 2026: 4.

25 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. 2025REFINE: Inversion-Free Backdoor Defense via Model Reprogrammingcited by 20
  4. 2025Rethinking Data Protection in the (Generative) Artificial Intelligence Eracited by 17
  5. 2023Deep Intellectual Property Protection: A Surveycited by 16
  6. 2025FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprintcited by 8
  7. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 6
  8. 2024DeepEclipse: How to Break White-Box DNN-Watermarking Schemescited by 4
  9. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  10. 2024RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protectioncited by 3
  11. 2025Watermarking Graph Neural Networks via Explanations for Ownership Protectioncited by 3
  12. 2026Floe: Federated Specialization for Real-Time LLM-SLM Inferencecited by 2
13 more
  1. 2025AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protectioncited by 1
  2. 2023Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platformcited by 0
  3. 2023FedReverse: Multiparty Reversible Deep Neural Network Watermarkingcited by 0
  4. 2024Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learningcited by 0
  5. 2024Watermarking Recommender Systemscited by 0
  6. 2025PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarkingcited by 0
  7. 2025Traceable Black-box Watermarks for Federated Learningcited by 0
  8. 2025FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learningcited by 0
  9. 2025Robust Client-Server Watermarking for Split Federated Learningcited by 0
  10. 2025Server-Enforced Watermarking in U-Shaped Split Federated Learningcited by 0
  11. 2026Collaborative Threshold Watermarkingcited by 0
  12. 2026FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMscited by 0
  13. 2026ZK-Trace: Certified Collusion Tracing with Zero-Knowledge Credentials for Federated GNSS Interference Monitoringcited by 0