Continual Learning principles play a crucial role in embedding watermarks for FL models within FedTracker. By incorporating CL techniques into the watermarking process, FedTracker can mitigate issues related to preserving model utility while adding watermarks without access to natural data. Specifically, FedTracker uses global memory accumulation and gradient projection based on past iterations' global gradients to optimize watermark insertion without compromising performance on the primary task. This adaptation of CL ensures that the watermarking process is efficient and effective in maintaining model fidelity.