Federated learning (FL), as a distributed machine learning method, enables model training while protecting user privacy and data security. However, the involvement of multiple parties and the widespread exposure of models in FL can easily lead to copyright leakage. This study proposes a watermarking scheme with ownership verification, model leakage tracing, and lazy client detection. The proposed scheme introduces a client identity-based backdoor watermark generation mechanism and federated dynamic weight adjustment (FDWA) to ensure the uniqueness of each client’s watermark and resolve watermark conflicts. Model fidelity and watermark trigger rates are significantly improved, while also achieving better performance in detecting lazy clients. Experimental results show that the scheme provides more comprehensive protection while maintaining model performance, significantly improves watermark trigger rates, and effectively resists various attacks such as fine-tuning, pruning, quantization, and collusion attacks, thus enhancing the security and fairness of the FL environment and providing effective copyright protection for models.