Shao Sujie, Wang Yue, Yang Chao, Liu Yan, Chen Xingyu, Qi Feng
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110006, China.
Sci Rep. 2024 Aug 21;14(1):19453. doi: 10.1038/s41598-024-70025-1.
Federated learning (FL) enables users to train the global model cooperatively without exposing their private data across the engaged parties, which is widely used in privacy-sensitive business. However, during the life cycle of FL models, both adversaries' attacks and ownership generalization threaten the FL models' copyright and affect the models' reliability. To address these problems, existing model watermarking techniques can be used to verify FL model's ownership. However, due to the lack of credible binding from "model extracted watermarks" to "ownership verification", it is difficult to form a closed-loop watermarking framework for copyright protection. Therefore, starting from the shortcomings of the current watermark verification scheme, this article proposed WFB, a blockchain-empowered watermarking framework for ownership verification of federated models. Firstly, we propose a improved watermark generation algorithm to solve the credibility issue of watermarks. Secondly, we propose a watermark embedding method in federated learning, while blockchain technology is used to ensure the credible storage of watermark information throughout the process. Thirdly, the credibility of ownership verification is improved because of the watermark authenticity. Experimental results demonstrate the fidelity, effectiveness and robustness of WFB, with other superiorities such as improving process security and traceability.
联邦学习（FL）使用户能够在不向参与方暴露其私有数据的情况下协同训练全局模型，这在对隐私敏感的业务中被广泛使用。然而，在联邦学习模型的生命周期中，对手的攻击和所有权泛化都威胁着联邦学习模型的版权，并影响模型的可靠性。为了解决这些问题，可以使用现有的模型水印技术来验证联邦学习模型的所有权。然而，由于缺乏从“模型提取的水印”到“所有权验证”的可信绑定，很难形成一个用于版权保护的闭环水印框架。因此，本文从当前水印验证方案的缺点出发，提出了WFB，一种用于联邦模型所有权验证的区块链赋能水印框架。首先，我们提出了一种改进的水印生成算法来解决水印的可信度问题。其次，我们提出了一种联邦学习中的水印嵌入方法，同时利用区块链技术确保水印信息在整个过程中的可信存储。第三，由于水印的真实性，提高了所有权验证的可信度。实验结果证明了WFB的保真度、有效性和鲁棒性，以及在提高过程安全性和可追溯性等其他优势。
