diff --git a/src/learner.cc b/src/learner.cc
index 8ee901482..08c59ba60 100644
--- a/src/learner.cc
+++ b/src/learner.cc
@@ -846,20 +846,9 @@ class LearnerConfiguration : public Learner {
   }
 
   void InitEstimation(MetaInfo const& info, linalg::Tensor<float, 1>* base_score) {
-#ifndef XGBOOST_USE_HIP
     base_score->Reshape(1);
     collective::ApplyWithLabels(info, base_score->Data(),
                                 [&] { UsePtr(obj_)->InitEstimation(info, base_score); });
-#else
-    if (info.IsVerticalFederated()) {
-        base_score->Reshape(1);
-        collective::ApplyWithLabels(info, base_score->Data()->HostPointer(),
-                sizeof(bst_float) * base_score->Size(),
-                [&] { UsePtr(obj_)->InitEstimation(info, base_score); });
-    } else {
-      UsePtr(obj_)->InitEstimation(info, base_score);
-    }
-#endif
   }
 };
 
@@ -1478,20 +1467,9 @@ class LearnerImpl : public LearnerIO {
  private:
   void GetGradient(HostDeviceVector<bst_float> const& preds, MetaInfo const& info,
                    std::int32_t iter, linalg::Matrix<GradientPair>* out_gpair) {
-#ifndef XGBOOST_USE_HIP
     out_gpair->Reshape(info.num_row_, this->learner_model_param_.OutputLength());
     collective::ApplyWithLabels(info, out_gpair->Data(),
                                 [&] { obj_->GetGradient(preds, info, iter, out_gpair); });
-#else
-    if (info.IsVerticalFederated()) {
-        out_gpair->Reshape(info.num_row_, this->learner_model_param_.OutputLength());
-        collective::ApplyWithLabels(info, out_gpair->Data(),
-                                [&] { obj_->GetGradient(preds, info, iter, out_gpair); });
-    }
-    else {
-      obj_->GetGradient(preds, info, iter, out_gpair);
-    }
-#endif
   }
 
   /*! \brief random number transformation seed. */