innsikt - Computer Science - # FL Model Protection Framework

FedTracker: Ownership Verification and Traceability for Federated Learning Models

Q: How does FedTracker address challenges related to preserving model utility during watermark embedding

FedTracker addresses the challenge of preserving model utility during watermark embedding by leveraging Continual Learning (CL) principles. By treating the primitive task and watermark embedding task as two separate tasks from different domains, FedTracker aims to reduce catastrophic forgetting that occurs when learning a new task. This approach helps maintain the utility of the model on both tasks by optimizing the gradients in a way that minimizes interference with previous knowledge. Additionally, FedTracker freezes certain layers, such as Batch Normalization layers, during retraining to prevent significant impact on model functionality.

Q: What are the implications of using Continual Learning principles in embedding watermarks for FL models

Continual Learning principles play a crucial role in embedding watermarks for FL models within FedTracker. By incorporating CL techniques into the watermarking process, FedTracker can mitigate issues related to preserving model utility while adding watermarks without access to natural data. Specifically, FedTracker uses global memory accumulation and gradient projection based on past iterations' global gradients to optimize watermark insertion without compromising performance on the primary task. This adaptation of CL ensures that the watermarking process is efficient and effective in maintaining model fidelity.

Q: How can FedTracker's approach to ownership verification and traceability be applied in other distributed learning paradigms

The approach taken by FedTracker for ownership verification and traceability can be applied in other distributed learning paradigms by adapting its framework to suit specific requirements and constraints of different scenarios. The bi-level protection scheme consisting of global watermarks for ownership verification and local fingerprints for traceability can be modified or extended based on the characteristics of alternative distributed learning frameworks. By customizing parameters like trigger sets or fingerprint generation methods according to unique data privacy concerns or collaborative training setups in various paradigms, similar mechanisms for verifying ownership and tracing malicious behavior among participants can be implemented effectively across diverse distributed learning environments.

Grunnleggende konsepter

FedTracker provides ownership verification and traceability for FL models through watermarking and local fingerprints.

Sammendrag

FedTracker introduces a novel framework for protecting FL models by embedding a global watermark and unique local fingerprints. The global watermark mechanism authenticates ownership, while local fingerprints identify the model's origin. The framework addresses challenges of utility preservation during watermark embedding and differentiation between Client models. FedTracker leverages Continual Learning principles to embed watermarks effectively. Experimental results demonstrate its effectiveness in ownership verification, traceability, fidelity, and robustness against attacks.

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arxiv.org

Statistikk

Global memory is defined as the accumulated global gradients from the first iteration to the current iteration.
The Fingerprint Similarity Score (FSS) measures the similarity of extracted fingerprints with continuous outputs.
WAFFLE proposes a server-side FL watermarking method using backdoor-based watermarks.

Sitater

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FedTracker

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

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

Dypere Spørsmål

How does FedTracker address challenges related to preserving model utility during watermark embedding

FedTracker addresses the challenge of preserving model utility during watermark embedding by leveraging Continual Learning (CL) principles. By treating the primitive task and watermark embedding task as two separate tasks from different domains, FedTracker aims to reduce catastrophic forgetting that occurs when learning a new task. This approach helps maintain the utility of the model on both tasks by optimizing the gradients in a way that minimizes interference with previous knowledge. Additionally, FedTracker freezes certain layers, such as Batch Normalization layers, during retraining to prevent significant impact on model functionality.

What are the implications of using Continual Learning principles in embedding watermarks for FL models

Continual Learning principles play a crucial role in embedding watermarks for FL models within FedTracker. By incorporating CL techniques into the watermarking process, FedTracker can mitigate issues related to preserving model utility while adding watermarks without access to natural data. Specifically, FedTracker uses global memory accumulation and gradient projection based on past iterations' global gradients to optimize watermark insertion without compromising performance on the primary task. This adaptation of CL ensures that the watermarking process is efficient and effective in maintaining model fidelity.

How can FedTracker's approach to ownership verification and traceability be applied in other distributed learning paradigms

The approach taken by FedTracker for ownership verification and traceability can be applied in other distributed learning paradigms by adapting its framework to suit specific requirements and constraints of different scenarios. The bi-level protection scheme consisting of global watermarks for ownership verification and local fingerprints for traceability can be modified or extended based on the characteristics of alternative distributed learning frameworks. By customizing parameters like trigger sets or fingerprint generation methods according to unique data privacy concerns or collaborative training setups in various paradigms, similar mechanisms for verifying ownership and tracing malicious behavior among participants can be implemented effectively across diverse distributed learning environments.