Protecting copyright in distributed learning is a challenge. Watermarking has emerged as a technique to safeguard deep neural networks, embedding distinctive signatures within model parameters or outputs to enable reliable ownership verification. Prevailing watermarking schemes address the ownership verification problems by mandating explicit client cooperation or assuming a trustworthy server. However, the empirical validation of these methods has focused exclusively on image-classification tasks, neglecting tabular datasets. This thesis proposes a robust algorithm for watermarking in federated learning for tabular data with attribution capabilities. Our approach embeds verifiable ownership signals directly into the global model while respecting the rigorous efficiency constraints characteristic of distributed and privacy-aware training.