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.