diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 4be35e9de..621c8b465 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -156,8 +156,9 @@ jobs:
     - name: Build and install XGBoost shared library
       run: |
         cd build
-        cmake .. -DBUILD_STATIC_LIB=OFF -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -GNinja
+        cmake .. -DBUILD_STATIC_LIB=OFF -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -GNinja -DPLUGIN_FEDERATED=ON -DGOOGLE_TEST=ON
         ninja -v install
+        ./testxgboost
         cd -
     - name: Build and run C API demo with shared
       run: |
diff --git a/doc/build.rst b/doc/build.rst
index cba75ff57..4a2d47c0f 100644
--- a/doc/build.rst
+++ b/doc/build.rst
@@ -134,7 +134,7 @@ From the command line on Linux starting from the XGBoost directory:
 
 .. note:: Specifying compute capability
 
-  To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DGPU_COMPUTE_VER=50``. A quick explanation and numbers for some architectures can be found `in this page <https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/>`_.
+  To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., ``-DCMAKE_CUDA_ARCHITECTURES=75``. A quick explanation and numbers for some architectures can be found `in this page <https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/>`_.
 
 .. note:: Faster distributed GPU training with NCCL
 
@@ -147,6 +147,8 @@ From the command line on Linux starting from the XGBoost directory:
     cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
     make -j4
 
+Some additional flags are available for NCCL, ``BUILD_WITH_SHARED_NCCL`` enables building XGBoost with NCCL as a shared library, while ``USE_DLOPEN_NCCL`` enables XGBoost  to load NCCL at runtime using ``dlopen``.
+
 On Windows, run CMake as follows:
 
 .. code-block:: bash
@@ -165,6 +167,17 @@ The above cmake configuration run will create an ``xgboost.sln`` solution file i
 
 To speed up compilation, run multiple jobs in parallel by appending option ``-- /MP``.
 
+Federated Learning
+==================
+
+The federated learning plugin requires ``grpc`` and ``protobuf``. To install grpc, refer
+to the `installation guide from the gRPC website
+<https://grpc.io/docs/languages/cpp/quickstart/>`_. Alternatively, one can use the
+``libgrpc`` and the ``protobuf`` package from conda forge if conda is available. After
+obtaining the required dependencies, enable the flag: `-DPLUGIN_FEDERATED=ON` when running
+CMake. Please note that only Linux is supported for the federated plugin.
+
+
 .. _build_python:
 
 ***********************************
@@ -228,11 +241,12 @@ There are several ways to build and install the package from source:
 
 3. Editable installation
 
-  To further enable rapid development and iteration, we provide an **editable installation**.
-  In an editable installation, the installed package is simply a symbolic link to your
-  working copy of the XGBoost source code. So every changes you make to your source
-  directory will be immediately visible to the Python interpreter. Here is how to
-  install XGBoost as editable installation:
+  To further enable rapid development and iteration, we provide an **editable
+  installation**.  In an editable installation, the installed package is simply a symbolic
+  link to your working copy of the XGBoost source code. So every changes you make to your
+  source directory will be immediately visible to the Python interpreter. To install
+  XGBoost as editable installation, first build the shared library as previously
+  described, then install the Python package:
 
   .. code-block:: bash
 
diff --git a/plugin/federated/README.md b/plugin/federated/README.md
index 631c44cee..1040c933e 100644
--- a/plugin/federated/README.md
+++ b/plugin/federated/README.md
@@ -1,33 +1,16 @@
 XGBoost Plugin for Federated Learning
 =====================================
 
-This folder contains the plugin for federated learning. Follow these steps to build and test it.
+This folder contains the plugin for federated learning.
 
-Install gRPC
-------------
-Refer to the [installation guide from the gRPC website](https://grpc.io/docs/languages/cpp/quickstart/).
+See [build instruction](../../doc/build.rst) for how to build the plugin.
 
-Build the Plugin
-----------------
-```shell
-# Under xgboost source tree.
-mkdir build
-cd build
-cmake .. -GNinja \
- -DPLUGIN_FEDERATED=ON \
- -DUSE_CUDA=ON\
- -DUSE_NCCL=ON
-ninja
-cd ../python-package
-pip install -e .
-```
-If CMake fails to locate gRPC, you may need to pass `-DCMAKE_PREFIX_PATH=<grpc path>` to CMake.
 
 Test Federated XGBoost
 ----------------------
 ```shell
 # Under xgboost source tree.
-cd tests/distributed
+cd tests/distributed/test_federated
 # This tests both CPU training (`hist`) and GPU training (`gpu_hist`).
 ./runtests-federated.sh
 ```
diff --git a/tests/ci_build/conda_env/cpp_test.yml b/tests/ci_build/conda_env/cpp_test.yml
index d08b904d8..99a4caef1 100644
--- a/tests/ci_build/conda_env/cpp_test.yml
+++ b/tests/ci_build/conda_env/cpp_test.yml
@@ -8,3 +8,5 @@ dependencies:
 - c-compiler
 - cxx-compiler
 - gtest
+- protobuf
+- libgrpc