Traceable Federated Learning Copyright Protection Method Based on Structural Embedding

Owing to the risk of copyright leakage in Federated Learning (FL) models caused by untrustworthy clients participating in joint training, current watermark embedding methods used by the central server face several challenges, such as incompatibility with secure FL architectures, insufficient traceability, and excessive server computational burden. Therefore, this study proposes a traceable and secure FL copyright protection scheme based on orthogonal constraints, abbreviated as FedSOW. Initially, the server replicates the convolutional layer embedded in the watermark to form a dual-channel layer and selects this dual-channel layer as the initial watermark layer. Subsequently, forward constraint rules are designed based on the principle of Schmidt orthogonalization, guiding the output features of the watermark layer of the client model using the orthogonal constraint. Finally, the client trains the watermark layer to form traceable local models with different orthogonal structures. Experimental results show that, compared with existing watermarking schemes, FedSOW demonstrates strong watermark persistence, ensuring copyright verification in the training round within the secure FL framework. Moreover, FedSOW exhibits excellent performance in terms of traceability, fidelity, and attack resistance.