Add P2P distributed optimization to advanced examples

.gitignore

@@ -91,6 +91,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+.mise.toml
 
 # Spyder project settings
 .spyderproject

examples/advanced/README.md

@@ -44,6 +44,10 @@ Please also install "./requirements.txt" in each example folder.
 * [Swarm Learning](./swarm_learning/README.md)
    * Example of swarm learning with NVIDIA FLARE using PyTorch with the CIFAR-10 dataset.
 
+## Distributed Optimization / P2P algorithms
+* [Distributed Optimization](./distributed_optimization/README.md)
+   * Example of using the low-level NVFlare APIs to implement and run P2P distributed optimization algorithms.
+
 ## Vertical Federated Learning
 * [Vertical Federated Learning](./vertical_federated_learning/README.md)
    * Example of running split learning using the CIFAR-10 dataset.

examples/advanced/distributed_optimization/.gitignore

@@ -0,0 +1,2 @@
+tmp
+data

examples/advanced/distributed_optimization/1-consensus/README.md

@@ -0,0 +1,2 @@
+# Consensus algorithm
+In this example we show how to run the consensus algorithm. You can find a detailed walkthrough in the [tutorial](tutorial.ipynb) or you can just run the provided [script](launcher.py) via `python launcher.py`.

examples/advanced/distributed_optimization/1-consensus/launcher.py

@@ -0,0 +1,63 @@
+# Copyright (c) 2025, 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 torch
+import random
+import matplotlib.pyplot as plt
+from nvflare.job_config.api import FedJob
+
+from nvflare.app_opt.p2p.controllers import P2PAlgorithmController
+from nvflare.app_opt.p2p.executors import ConsensusExecutor
+from nvflare.app_opt.p2p.types import Config
+from nvflare.app_opt.p2p.utils.config_generator import generate_random_network
+
+
+class CustomConsensusExecutor(ConsensusExecutor):
+    def __init__(self):
+        super().__init__(initial_value=random.randint(0, 10))
+
+
+if __name__ == "__main__":
+    # Create job
+    job = FedJob(name="consensus")
+
+    # generate random config
+    num_clients = 6
+    network, _ = generate_random_network(num_clients=num_clients)
+    config = Config(network=network, extra={"iterations": 50})
+
+    # send controller to server
+    controller = P2PAlgorithmController(config=config)
+    job.to_server(controller)
+
+    # Add clients
+    for i in range(num_clients):
+        executor = CustomConsensusExecutor()
+        job.to(executor, f"site-{i + 1}")
+
+    # run
+    job.export_job("./tmp/job_configs")
+    job.simulator_run("./tmp/runs/consensus")
+
+    history = {
+        f"site-{i + 1}": torch.load(
+            f"tmp/runs/consensus/site-{i + 1}/value_sequence.pt"
+        )
+        for i in range(num_clients)
+    }
+    plt.figure()
+    for i in range(num_clients):
+        plt.plot(history[f"site-{i + 1}"], label=f"site-{i + 1}")
+    plt.legend()
+    plt.title("Evolution of local values")
+    plt.show()

examples/advanced/distributed_optimization/2-two_moons/README.md

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+# Distributed classification - two moons dataset
+
+In this example we consider the simple [two moons](https://scikit-learn.org/dev/modules/generated/sklearn.datasets.make_moons.html) classification problem and compare different distributed optimization algorithms:
+- Distributed gradient descent
+- Gradient tracking
+- GTAdam
+
+We run all the algorithms with 6 clients, for 1000 iterations and with a stepsize of 0.01. These common parameters can be changed in the `config.py` file.
+
+The models and datasets are stored in `utils.py` and are the same for all algorithms.
+
+## Distributed gradient descent
+```
+python launcher_dgd.py
+```
+![dgd](dgd_results.png)
+
+## Gradient tracking
+```
+python launcher_gt.py
+```
+![gt](gt_results.png)
+
+## GTAdam
+```
+python launcher_gtadam.py
+```
+![gtadam](gtadam_results.png)

examples/advanced/distributed_optimization/2-two_moons/config.py

@@ -0,0 +1,16 @@
+# Copyright (c) 2025, 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.
+NUM_CLIENTS = 6
+ITERATIONS = 1000
+STEPSIZE = 0.01

examples/advanced/distributed_optimization/2-two_moons/launcher_dgd.py

@@ -0,0 +1,63 @@
+# Copyright (c) 2025, 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 torch
+from config import ITERATIONS, NUM_CLIENTS, STEPSIZE
+from utils import NeuralNetwork, get_dataloaders, plot_results
+
+from nvflare.app_opt.p2p.controllers import P2PAlgorithmController
+from nvflare.app_opt.p2p.executors import DGDExecutor
+from nvflare.app_opt.p2p.types import Config
+from nvflare.app_opt.p2p.utils.config_generator import generate_random_network
+from nvflare.job_config.api import FedJob
+
+
+class CustomDGDExecutor(DGDExecutor):
+    def __init__(self, data_seed: int | None = None):
+        self._data_seed = data_seed
+        train_dataloader, test_dataloader = get_dataloaders(data_seed)
+        super().__init__(
+            model=NeuralNetwork(),
+            loss=torch.nn.CrossEntropyLoss(),
+            train_dataloader=train_dataloader,
+            test_dataloader=test_dataloader,
+        )
+
+
+if __name__ == "__main__":
+    # Create job
+    job_name = "dgd"
+    job = FedJob(name=job_name)
+
+    # generate random config
+    network, _ = generate_random_network(num_clients=NUM_CLIENTS)
+    config = Config(
+        network=network,
+        extra={"iterations": ITERATIONS, "stepsize": STEPSIZE},
+    )
+
+    # send controller to server
+    controller = P2PAlgorithmController(config=config)
+    job.to_server(controller)
+
+    # Add clients
+    for i in range(NUM_CLIENTS):
+        executor = CustomDGDExecutor(data_seed=i)
+        job.to(executor, f"site-{i + 1}")
+
+    # run
+    job.export_job("./tmp/job_configs")
+    job.simulator_run(f"./tmp/runs/{job_name}")
+
+    # plot and save results
+    plot_results(job_name, NUM_CLIENTS)

examples/advanced/distributed_optimization/2-two_moons/launcher_gt.py

@@ -0,0 +1,61 @@
+# Copyright (c) 2025, 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 torch
+from config import ITERATIONS, NUM_CLIENTS, STEPSIZE
+from utils import NeuralNetwork, get_dataloaders, plot_results
+
+from nvflare.app_opt.p2p.controllers import P2PAlgorithmController
+from nvflare.app_opt.p2p.executors import GTExecutor
+from nvflare.app_opt.p2p.types import Config
+from nvflare.app_opt.p2p.utils.config_generator import generate_random_network
+from nvflare.job_config.api import FedJob
+
+
+class CustomGTExecutor(GTExecutor):
+    def __init__(self, data_seed: int | None = None):
+        self._data_seed = data_seed
+        train_dataloader, test_dataloader = get_dataloaders(data_seed)
+        super().__init__(
+            model=NeuralNetwork(),
+            loss=torch.nn.CrossEntropyLoss(),
+            train_dataloader=train_dataloader,
+            test_dataloader=test_dataloader,
+        )
+
+
+if __name__ == "__main__":
+    # Create job
+    job_name = "gt"
+    job = FedJob(name=job_name)
+
+    # generate random config
+    network, _ = generate_random_network(num_clients=NUM_CLIENTS)
+    config = Config(
+        network=network, extra={"iterations": ITERATIONS, "stepsize": STEPSIZE}
+    )
+
+    # send controller to server
+    controller = P2PAlgorithmController(config=config)
+    job.to_server(controller)
+
+    # Add clients
+    for i in range(NUM_CLIENTS):
+        executor = CustomGTExecutor(data_seed=i)
+        job.to(executor, f"site-{i + 1}")
+
+    # run
+    job.export_job("./tmp/job_configs")
+    job.simulator_run(f"./tmp/runs/{job_name}")
+
+    plot_results(job_name, NUM_CLIENTS)

examples/advanced/distributed_optimization/2-two_moons/launcher_gtadam.py

@@ -0,0 +1,67 @@
+# Copyright (c) 2025, 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 torch
+from config import ITERATIONS, NUM_CLIENTS, STEPSIZE
+from utils import NeuralNetwork, get_dataloaders, plot_results
+
+from nvflare.app_opt.p2p.controllers import P2PAlgorithmController
+from nvflare.app_opt.p2p.executors import GTADAMExecutor
+from nvflare.app_opt.p2p.types import Config
+from nvflare.app_opt.p2p.utils.config_generator import generate_random_network
+from nvflare.job_config.api import FedJob
+
+
+class CustomGTADAMExecutor(GTADAMExecutor):
+    def __init__(self, data_seed: int | None = None):
+        self._data_seed = data_seed
+        train_dataloader, test_dataloader = get_dataloaders(data_seed)
+        super().__init__(
+            model=NeuralNetwork(),
+            loss=torch.nn.CrossEntropyLoss(),
+            train_dataloader=train_dataloader,
+            test_dataloader=test_dataloader,
+        )
+
+if __name__ == "__main__":
+    # Create job
+    job_name = "gtadam"
+    job = FedJob(name=job_name)
+
+    # generate random config
+    network, _ = generate_random_network(num_clients=NUM_CLIENTS)
+    config = Config(
+        network=network,
+        extra={
+            "iterations": ITERATIONS,
+            "stepsize": STEPSIZE,
+            "beta1": 0.9,
+            "beta2": 0.999,
+            "epsilon": 1e-8,
+        },
+    )
+
+    # send controller to server
+    controller = P2PAlgorithmController(config=config)
+    job.to_server(controller)
+
+    # Add clients
+    for i in range(NUM_CLIENTS):
+        executor = CustomGTADAMExecutor(data_seed=i)
+        job.to(executor, f"site-{i + 1}")
+
+    # run
+    job.export_job("./tmp/job_configs")
+    job.simulator_run(f"./tmp/runs/{job_name}")
+
+    plot_results(job_name, NUM_CLIENTS)