Federated　learning　(FL)　is　a　novel　machine　learning　framework　in　which　machine　learning　models　are　built　jointly　by　multiple　parties.　We　investigate　the　privacy　preservation　of　XGBoost,　a　gradient　boosting　decision　tree　(GBDT)　model,　in　the　context　of　FL.　While　recent　work　relies　on　cryptographic　schemes　to　preserve　the　privacy　of　model　gradients,　these　methods　are　computationally　expensive.　In　this　paper,　we　propose　an　adaptive　gradient　privacy-preserving　algorithm　based　on　differential　privacy　(DP),　which　is　more　computationally　efficient.　Our　algorithm　perturbs　individual　data　by　computing　an　adaptive　gradient　mean　per　sample　and　adding　appropriate　noise　during　XGBoost　training,　while　still　making　the　perturbed　gradient　data　available.　The　training　accuracy　and　communication　efficiency　of　the　model　are　guaranteed　under　the　premise　of　satisfying　the　definition　of　DP.　We　show　the　proposed　algorithm　outperforms　other　DP　methods　in　terms　of　prediction　accuracy　and　approaches　the　lossless　federated　XGBoost　model　while　being　more　efficient.　©　2023　ACM.