Chapter 6 working in progress

examples/tutorials/self-paced-training/part-3_security_and_privacy/chapter-6_Security_in_federated_compute_system/06.3_site_security/examples/custom_client_side_job_level_authorization/code/data/download.py

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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This Dirichlet sampling strategy for creating a heterogeneous partition is adopted
+# from FedMA (https://github.com/IBM/FedMA).
+
+# MIT License
+
+# Copyright (c) 2020 International Business Machines
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import argparse
+
+import torchvision.datasets as datasets
+
+# default dataset path
+CIFAR10_ROOT = "/tmp/nvflare/data/cifar10"
+
+
+def define_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset_path", type=str, default=CIFAR10_ROOT, nargs="?")
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    datasets.CIFAR10(root=args.dataset_path, train=True, download=True)
+    datasets.CIFAR10(root=args.dataset_path, train=False, download=True)
+
+
+if __name__ == "__main__":
+    main(define_parser())

examples/tutorials/self-paced-training/part-3_security_and_privacy/chapter-6_Security_in_federated_compute_system/06.3_site_security/examples/custom_client_side_job_level_authorization/code/fl_jobs.py

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+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+
+from src.fedavg import FedAvg
+from src.network import SimpleNetwork
+
+from nvflare.job_config.api import FedJob
+from nvflare.job_config.script_runner import ScriptRunner
+
+if __name__ == "__main__":
+    num_clients = 2
+    num_rounds = 2
+    job_names = ["fedavg", "secret-job"] 
+    train_script = "src/client.py"
+    config_dir = "/tmp/nvflare/jobs/workdir"
+
+    for job_name in job_names:
+        job = FedJob(name=job_name, min_clients=num_clients)
+        controller = FedAvg(
+            stop_cond="accuracy > 25",
+            save_filename="global_model.pt",
+            initial_model=SimpleNetwork(),
+            num_clients=num_clients,
+            num_rounds=num_rounds,
+        )
+
+        job.to_server(controller)
+
+        # Add clients
+        for i in range(num_clients):
+            executor = ScriptRunner(script=train_script, script_args="")
+            job.to(executor, f"site-{i+1}")
+
+        job_config_dir = os.path.join(config_dir, job_name)
+        print(f"job-config for {job_name} is at ",job_config_dir)
+        job.export_job(config_dir)
+       # job.simulator_run(config_dir)
+
+

examples/tutorials/self-paced-training/part-3_security_and_privacy/chapter-6_Security_in_federated_compute_system/06.3_site_security/examples/custom_client_side_job_level_authorization/code/requirements.txt

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+torch
+torchvision
+tensorboard