Add demo for vertical federated learning (#9103)

demo/nvflare/horizontal/README.md

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+# Experimental Support of Horizontal Federated XGBoost using NVFlare
+
+This directory contains a demo of Horizontal Federated Learning using
+[NVFlare](https://nvidia.github.io/NVFlare/).
+
+## Training with CPU only
+
+To run the demo, first build XGBoost with the federated learning plugin enabled (see the
+[README](../../plugin/federated/README.md)).
+
+Install NVFlare (note that currently NVFlare only supports Python 3.8):
+```shell
+pip install nvflare
+```
+
+Prepare the data:
+```shell
+./prepare_data.sh
+```
+
+Start the NVFlare federated server:
+```shell
+/tmp/nvflare/poc/server/startup/start.sh
+```
+
+In another terminal, start the first worker:
+```shell
+/tmp/nvflare/poc/site-1/startup/start.sh
+```
+
+And the second worker:
+```shell
+/tmp/nvflare/poc/site-2/startup/start.sh
+```
+
+Then start the admin CLI:
+```shell
+/tmp/nvflare/poc/admin/startup/fl_admin.sh
+```
+
+In the admin CLI, run the following command:
+```shell
+submit_job horizontal-xgboost
+```
+
+Once the training finishes, the model file should be written into
+`/tmp/nvlfare/poc/site-1/run_1/test.model.json` and `/tmp/nvflare/poc/site-2/run_1/test.model.json`
+respectively.
+
+Finally, shutdown everything from the admin CLI, using `admin` as password:
+```shell
+shutdown client
+shutdown server
+```
+
+## Training with GPUs
+
+To demo with Federated Learning using GPUs, make sure your machine has at least 2 GPUs.
+Build XGBoost with the federated learning plugin enabled along with CUDA, but with NCCL
+turned off (see the [README](../../plugin/federated/README.md)).
+
+Modify `config/config_fed_client.json` and set `use_gpus` to `true`, then repeat the steps
+above.

demo/nvflare/horizontal/custom/controller.py

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+"""
+Example of training controller with NVFlare
+===========================================
+"""
+import multiprocessing
+
+from nvflare.apis.client import Client
+from nvflare.apis.fl_context import FLContext
+from nvflare.apis.impl.controller import Controller, Task
+from nvflare.apis.shareable import Shareable
+from nvflare.apis.signal import Signal
+from trainer import SupportedTasks
+
+import xgboost.federated
+
+
+class XGBoostController(Controller):
+    def __init__(self, port: int, world_size: int, server_key_path: str,
+                 server_cert_path: str, client_cert_path: str):
+        """Controller for federated XGBoost.
+
+        Args:
+            port: the port for the gRPC server to listen on.
+            world_size: the number of sites.
+            server_key_path: the path to the server key file.
+            server_cert_path: the path to the server certificate file.
+            client_cert_path: the path to the client certificate file.
+        """
+        super().__init__()
+        self._port = port
+        self._world_size = world_size
+        self._server_key_path = server_key_path
+        self._server_cert_path = server_cert_path
+        self._client_cert_path = client_cert_path
+        self._server = None
+
+    def start_controller(self, fl_ctx: FLContext):
+        self._server = multiprocessing.Process(
+            target=xgboost.federated.run_federated_server,
+            args=(self._port, self._world_size, self._server_key_path,
+                  self._server_cert_path, self._client_cert_path))
+        self._server.start()
+
+    def stop_controller(self, fl_ctx: FLContext):
+        if self._server:
+            self._server.terminate()
+
+    def process_result_of_unknown_task(self, client: Client, task_name: str,
+                                       client_task_id: str, result: Shareable,
+                                       fl_ctx: FLContext):
+        self.log_warning(fl_ctx, f"Unknown task: {task_name} from client {client.name}.")
+
+    def control_flow(self, abort_signal: Signal, fl_ctx: FLContext):
+        self.log_info(fl_ctx, "XGBoost training control flow started.")
+        if abort_signal.triggered:
+            return
+        task = Task(name=SupportedTasks.TRAIN, data=Shareable())
+        self.broadcast_and_wait(
+            task=task,
+            min_responses=self._world_size,
+            fl_ctx=fl_ctx,
+            wait_time_after_min_received=1,
+            abort_signal=abort_signal,
+        )
+        if abort_signal.triggered:
+            return
+
+        self.log_info(fl_ctx, "XGBoost training control flow finished.")

demo/nvflare/horizontal/custom/trainer.py

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+import os
+
+from nvflare.apis.executor import Executor
+from nvflare.apis.fl_constant import FLContextKey, ReturnCode
+from nvflare.apis.fl_context import FLContext
+from nvflare.apis.shareable import Shareable, make_reply
+from nvflare.apis.signal import Signal
+
+import xgboost as xgb
+from xgboost import callback
+
+
+class SupportedTasks(object):
+    TRAIN = "train"
+
+
+class XGBoostTrainer(Executor):
+    def __init__(self, server_address: str, world_size: int, server_cert_path: str,
+                 client_key_path: str, client_cert_path: str, use_gpus: bool):
+        """Trainer for federated XGBoost.
+
+        Args:
+            server_address: address for the gRPC server to connect to.
+            world_size: the number of sites.
+            server_cert_path: the path to the server certificate file.
+            client_key_path: the path to the client key file.
+            client_cert_path: the path to the client certificate file.
+        """
+        super().__init__()
+        self._server_address = server_address
+        self._world_size = world_size
+        self._server_cert_path = server_cert_path
+        self._client_key_path = client_key_path
+        self._client_cert_path = client_cert_path
+        self._use_gpus = use_gpus
+
+    def execute(self, task_name: str, shareable: Shareable, fl_ctx: FLContext,
+                abort_signal: Signal) -> Shareable:
+        self.log_info(fl_ctx, f"Executing {task_name}")
+        try:
+            if task_name == SupportedTasks.TRAIN:
+                self._do_training(fl_ctx)
+                return make_reply(ReturnCode.OK)
+            else:
+                self.log_error(fl_ctx, f"{task_name} is not a supported task.")
+                return make_reply(ReturnCode.TASK_UNKNOWN)
+        except BaseException as e:
+            self.log_exception(fl_ctx,
+                               f"Task {task_name} failed. Exception: {e.__str__()}")
+            return make_reply(ReturnCode.EXECUTION_EXCEPTION)
+
+    def _do_training(self, fl_ctx: FLContext):
+        client_name = fl_ctx.get_prop(FLContextKey.CLIENT_NAME)
+        rank = int(client_name.split('-')[1]) - 1
+        communicator_env = {
+            'xgboost_communicator': 'federated',
+            'federated_server_address': self._server_address,
+            'federated_world_size': self._world_size,
+            'federated_rank': rank,
+            'federated_server_cert': self._server_cert_path,
+            'federated_client_key': self._client_key_path,
+            'federated_client_cert': self._client_cert_path
+        }
+        with xgb.collective.CommunicatorContext(**communicator_env):
+            # Load file, file will not be sharded in federated mode.
+            dtrain = xgb.DMatrix('agaricus.txt.train')
+            dtest = xgb.DMatrix('agaricus.txt.test')
+
+            # Specify parameters via map, definition are same as c++ version
+            param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
+            if self._use_gpus:
+                self.log_info(fl_ctx, f'Training with GPU {rank}')
+                param['tree_method'] = 'gpu_hist'
+                param['gpu_id'] = rank
+
+            # Specify validations set to watch performance
+            watchlist = [(dtest, 'eval'), (dtrain, 'train')]
+            num_round = 20
+
+            # Run training, all the features in training API is available.
+            bst = xgb.train(param, dtrain, num_round, evals=watchlist,
+                            early_stopping_rounds=2, verbose_eval=False,
+                            callbacks=[callback.EvaluationMonitor(rank=rank)])
+
+            # Save the model.
+            workspace = fl_ctx.get_prop(FLContextKey.WORKSPACE_OBJECT)
+            run_number = fl_ctx.get_prop(FLContextKey.CURRENT_RUN)
+            run_dir = workspace.get_run_dir(run_number)
+            bst.save_model(os.path.join(run_dir, "test.model.json"))
+            xgb.collective.communicator_print("Finished training\n")

demo/nvflare/horizontal/prepare_data.sh

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+#!/bin/bash
+
+set -e
+
+rm -fr ./agaricus* ./*.pem ./poc
+
+world_size=2
+
+# Generate server and client certificates.
+openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout server-key.pem -out server-cert.pem -subj "/C=US/CN=localhost"
+openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout client-key.pem -out client-cert.pem -subj "/C=US/CN=localhost"
+
+# Split train and test files manually to simulate a federated environment.
+split -n l/${world_size} --numeric-suffixes=1 -a 1 ../data/agaricus.txt.train agaricus.txt.train-site-
+split -n l/${world_size} --numeric-suffixes=1 -a 1 ../data/agaricus.txt.test agaricus.txt.test-site-
+
+nvflare poc -n 2 --prepare
+mkdir -p /tmp/nvflare/poc/admin/transfer/horizontal-xgboost
+cp -fr config custom /tmp/nvflare/poc/admin/transfer/horizontal-xgboost
+cp server-*.pem client-cert.pem /tmp/nvflare/poc/server/
+for id in $(eval echo "{1..$world_size}"); do
+  cp server-cert.pem client-*.pem /tmp/nvflare/poc/site-"$id"/
+  cp agaricus.txt.train-site-"$id" /tmp/nvflare/poc/site-"$id"/agaricus.txt.train
+  cp agaricus.txt.test-site-"$id" /tmp/nvflare/poc/site-"$id"/agaricus.txt.test
+done