FedIPR: Ownership Verification for Federated Deep Neural Network Models

TL;DR

This paper introduces FedIPR, a privacy-preserving ownership verification scheme for federated deep neural networks that embeds watermarks allowing clients to verify model ownership without exposing private data.

Contribution

It proposes a novel watermarking scheme enabling private ownership verification in federated learning models, ensuring security and robustness against attacks.

Findings

Watermarks can be embedded and detected reliably across tasks.

The scheme is resilient to removal attacks.

Model performance remains unaffected by watermarking.

Abstract

Federated learning models are collaboratively developed upon valuable training data owned by multiple parties. During the development and deployment of federated models, they are exposed to risks including illegal copying, re-distribution, misuse and/or free-riding. To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i.e., FedIPR. We propose a novel federated deep neural network (FedDNN) ownership verification scheme that allows private watermarks to be embedded and verified to claim legitimate IPR of FedDNN models. In the proposed scheme, each client independently verifies the existence of the model watermarks and claims respective ownership of the federated model without disclosing neither private training data nor private watermark information. The effectiveness of embedded watermarks is theoretically justified by the rigorous analysis of conditions under which watermarks can be privately embedded and detected by multiple clients. Moreover, extensive experimental results on computer vision and natural language processing tasks demonstrate that varying bit-length watermarks can be embedded and reliably detected without compromising original model performances. Our watermarking scheme is also resilient to various federated training settings and robust against removal attacks.