indsigt - Computer Science - # FL Model Protection Framework

FedTracker: Ownership Verification and Traceability in Federated Learning Models

Q: How can FedTracker ensure robustness against watermark removal attacks

In order to ensure robustness against watermark removal attacks, FedTracker implements several strategies. Firstly, it embeds a global watermark using backdoor-based techniques that can be verified through black-box access. This makes it harder for adversaries to remove the watermark without detection. Secondly, FedTracker utilizes Continual Learning (CL) principles to embed the watermark in a way that preserves the utility of the FL model on both primitive and watermark tasks. By incorporating CL, FedTracker reduces catastrophic forgetting during watermark embedding, making it more resilient against removal attempts. Additionally, FedTracker employs local fingerprints for traceability, which adds an extra layer of protection as each Client's model is uniquely identified with a fingerprint.

Q: What implications does CL-based watermark embedding have on FL model performance

The implementation of CL-based watermark embedding in FedTracker has significant implications on FL model performance. By treating the primitive task and the watermark embedding task as two different domains from a CL perspective, FedTracker aims to reduce catastrophic forgetting when learning new tasks like embedding watermarks without natural data access. This approach helps maintain the utility of the FL model by ensuring that retraining on out-of-distribution trigger sets does not compromise its functionality significantly. As a result, models trained using FedTracker are less likely to experience degradation in performance due to watermarks being embedded.

Q: How can ownership verification impact legal proceedings outside the FL group

Ownership verification plays a crucial role in legal proceedings outside the FL group by providing concrete evidence of copyright infringement and unauthorized distribution of FL models by malicious parties or individuals within the group. When ownership is verified through mechanisms like those implemented in FedTracker, such as extracting watermarks or verifying unique identifiers embedded in models, it strengthens legal claims against unauthorized users who have distributed or sold copyrighted material without consent from the rightful owners – i.e., other participants within an FL group.

Kernekoncepter

The author presents FedTracker, a framework providing ownership verification and traceability in FL models using watermarking and local fingerprints.

Resumé

FedTracker introduces a bi-level protection scheme with global watermarking for ownership verification and local fingerprints for traceability. It addresses challenges of utility preservation during watermark embedding and differentiation between Client models.

Key points:

Federated Learning (FL) faces model leakage issues due to malicious clients.
Ownership verification and traceability are crucial for protecting FL model copyright.
FedTracker embeds global watermarks and local fingerprints to address these concerns effectively.
Challenges include preserving model utility during watermark embedding and distinguishing different Client models.

Tilpas resumé

Genskriv med AI

Generer citater

Oversæt kilde

Til et andet sprog

Generer mindmap

fra kildeindhold

Besøg kilde

arxiv.org

Statistik

Ownership verification refers to proving the suspicious model belongs to the FL group.
Traceability involves tracing the stolen model back to the malicious client in FL.

Citater

Vigtigste indsigter udtrukket fra

FedTracker

by Shuo Shao,We... kl. arxiv.org 03-05-2024

https://arxiv.org/pdf/2211.07160.pdf

Dybere Forespørgsler

How can FedTracker ensure robustness against watermark removal attacks

In order to ensure robustness against watermark removal attacks, FedTracker implements several strategies. Firstly, it embeds a global watermark using backdoor-based techniques that can be verified through black-box access. This makes it harder for adversaries to remove the watermark without detection. Secondly, FedTracker utilizes Continual Learning (CL) principles to embed the watermark in a way that preserves the utility of the FL model on both primitive and watermark tasks. By incorporating CL, FedTracker reduces catastrophic forgetting during watermark embedding, making it more resilient against removal attempts. Additionally, FedTracker employs local fingerprints for traceability, which adds an extra layer of protection as each Client's model is uniquely identified with a fingerprint.

What implications does CL-based watermark embedding have on FL model performance

The implementation of CL-based watermark embedding in FedTracker has significant implications on FL model performance. By treating the primitive task and the watermark embedding task as two different domains from a CL perspective, FedTracker aims to reduce catastrophic forgetting when learning new tasks like embedding watermarks without natural data access. This approach helps maintain the utility of the FL model by ensuring that retraining on out-of-distribution trigger sets does not compromise its functionality significantly. As a result, models trained using FedTracker are less likely to experience degradation in performance due to watermarks being embedded.

How can ownership verification impact legal proceedings outside the FL group

Ownership verification plays a crucial role in legal proceedings outside the FL group by providing concrete evidence of copyright infringement and unauthorized distribution of FL models by malicious parties or individuals within the group. When ownership is verified through mechanisms like those implemented in FedTracker, such as extracting watermarks or verifying unique identifiers embedded in models, it strengthens legal claims against unauthorized users who have distributed or sold copyrighted material without consent from the rightful owners – i.e., other participants within an FL group.