Federated　learning　(FL)　allows　multiple　participants　to　collaboratively　build　deep　learning　(DL)　models　without　directly　sharing　data.　Consequently,　the　issue　of　copyright　protection　in　FL　becomes　important　since　unreliable　participants　may　gain　access　to　the　jointly　trained　model.　Application　of　homomorphic　encryption　(HE)　in　a　secure　FL　framework　prevents　the　central　server　from　accessing　plaintext　models.　Thus,　it　is　no　longer　feasible　to　embed　the　watermark　at　the　central　server　using　existing　watermarking　schemes.　In　this　article,　we　propose　a　novel　client-side　FL　watermarking　scheme　to　tackle　the　copyright　protection　issue　in　secure　FL　with　HE.　To　the　best　of　our　knowledge,　it　is　the　first　scheme　to　embed　the　watermark　to　models　under　a　secure　FL　environment.　We　design　a　black-box　watermarking　scheme　based　on　client-side　backdooring　to　embed　a　pre-designed　trigger　set　into　an　FL　model　by　a　gradient-enhanced　embedding　method.　Additionally,　we　propose　a　trigger　set　construction　mechanism　to　ensure　that　the　watermark　cannot　be　forged.　Experimental　results　demonstrate　that　our　proposed　scheme　delivers　outstanding　protection　performance　and　robustness　against　various　watermark　removal　attacks　and　ambiguity　attack.　©　2023　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.