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Add custom strategy tutorial #1623

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merged 9 commits into from
Feb 5, 2023
Merged

Add custom strategy tutorial #1623

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b5a9983
Add custom strategy
adam-narozniak Feb 2, 2023
7418b81
Merge branch 'main' into custom-strategy-tutorial
danieljanes Feb 3, 2023
6015f7f
Fix indentation to decrease diffs
adam-narozniak Feb 3, 2023
6a3a02d
Merge branch 'custom-strategy-tutorial' of github.com:adap/flower int…
adam-narozniak Feb 3, 2023
ca60a1f
Merge branch 'main' into custom-strategy-tutorial
danieljanes Feb 3, 2023
a35d6ba
Update doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb
danieljanes Feb 5, 2023
914c889
Update doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb
danieljanes Feb 5, 2023
d3229d1
Update doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb
danieljanes Feb 5, 2023
d8cbf0e
Merge branch 'main' into custom-strategy-tutorial
danieljanes Feb 5, 2023
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102 changes: 84 additions & 18 deletions doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -320,7 +320,7 @@
"source": [
"## Build a Strategy from scratch\n",
"\n",
"[WIP - add description]"
"Let’s overwrite the `configure_fit` method such that it passes a higher learning rate (potentially also other hyperparameters) to the optimizer of a fraction of the clients. We will keep the sampling of the clients as it is in `FedAvg` and then change the configuration dictionary (one of the `FitIns` attributes)."
]
},
{
Expand All @@ -331,6 +331,7 @@
},
"outputs": [],
"source": [
"from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg\n",
"from typing import Callable, Union\n",
"\n",
"from flwr.common import (\n",
Expand All @@ -350,6 +351,22 @@
"\n",
"\n",
"class FedCustom(fl.server.strategy.Strategy):\n",
"\n",
" def __init__(\n",
" self,\n",
" fraction_fit: float = 1.0,\n",
" fraction_evaluate: float = 1.0,\n",
" min_fit_clients: int = 2,\n",
" min_evaluate_clients: int = 2,\n",
" min_available_clients: int = 2\n",
" ) -> None:\n",
" super().__init__()\n",
" self.fraction_fit = fraction_fit\n",
" self.fraction_evaluate = fraction_evaluate\n",
" self.min_fit_clients = min_fit_clients\n",
" self.min_evaluate_clients = min_evaluate_clients\n",
" self.min_available_clients = min_available_clients\n",
"\n",
" def __repr__(self) -> str:\n",
" return \"FedCustom\"\n",
"\n",
Expand All @@ -366,9 +383,26 @@
" ) -> List[Tuple[ClientProxy, FitIns]]:\n",
" \"\"\"Configure the next round of training.\"\"\"\n",
"\n",
" # TODO WIP - add implementation\n",
" \n",
" return []\n",
" # Sample clients\n",
" sample_size, min_num_clients = self.num_fit_clients(\n",
" client_manager.num_available()\n",
" )\n",
" clients = client_manager.sample(\n",
" num_clients=sample_size, min_num_clients=min_num_clients\n",
" )\n",
"\n",
" # Create custom configs\n",
" n_clients = len(clients)\n",
" half_clients = n_clients // 2\n",
" standard_config = { \"lr\": 0.001 }\n",
" higher_lr_config = { \"lr\": 0.003 }\n",
" fit_configurations = []\n",
" for idx, client in enumerate(clients):\n",
" if idx < half_clients:\n",
" fit_configurations.append((client, FitIns(parameters, standard_config)))\n",
" else:\n",
" fit_configurations.append((client, FitIns(parameters, higher_lr_config)))\n",
" return fit_configurations\n",
"\n",
" def aggregate_fit(\n",
" self,\n",
Expand All @@ -378,18 +412,33 @@
" ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:\n",
" \"\"\"Aggregate fit results using weighted average.\"\"\"\n",
"\n",
" # TODO WIP - add implementation\n",
"\n",
" return None, {}\n",
" weights_results = [\n",
" (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)\n",
" for _, fit_res in results\n",
" ]\n",
" parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))\n",
" metrics_aggregated = {}\n",
" return parameters_aggregated, metrics_aggregated\n",
"\n",
" def configure_evaluate(\n",
" self, server_round: int, parameters: Parameters, client_manager: ClientManager\n",
" ) -> List[Tuple[ClientProxy, EvaluateIns]]:\n",
" \"\"\"Configure the next round of evaluation.\"\"\"\n",
"\n",
" # TODO WIP - add implementation\n",
"\n",
" return []\n",
" if self.fraction_evaluate == 0.0:\n",
" return []\n",
" config = {}\n",
" evaluate_ins = EvaluateIns(parameters, config)\n",
"\n",
" # Sample clients\n",
" sample_size, min_num_clients = self.num_evaluation_clients(\n",
" client_manager.num_available()\n",
" )\n",
" clients = client_manager.sample(\n",
" num_clients=sample_size, min_num_clients=min_num_clients\n",
" )\n",
"\n",
" # Return client/config pairs\n",
" return [(client, evaluate_ins) for client in clients]\n",
"\n",
" def aggregate_evaluate(\n",
" self,\n",
Expand All @@ -399,18 +448,35 @@
" ) -> Tuple[Optional[float], Dict[str, Scalar]]:\n",
" \"\"\"Aggregate evaluation losses using weighted average.\"\"\"\n",
"\n",
" # TODO WIP - add implementation\n",
" if not results:\n",
" return None, {}\n",
"\n",
" return None, {}\n",
" loss_aggregated = weighted_loss_avg(\n",
" [\n",
" (evaluate_res.num_examples, evaluate_res.loss)\n",
" for _, evaluate_res in results\n",
" ]\n",
" )\n",
" metrics_aggregated = {}\n",
" return loss_aggregated, metrics_aggregated\n",
"\n",
" def evaluate(\n",
" self, server_round: int, parameters: Parameters\n",
" ) -> Optional[Tuple[float, Dict[str, Scalar]]]:\n",
" \"\"\"Evaluate model parameters using an evaluation function.\"\"\"\n",
" \"\"\"Evaluate global model parameters using an evaluation function.\"\"\"\n",
"\n",
" # TODO WIP - add implementation\n",
" # Let's assume we won't perform the global model evaluation on the server side.\n",
" return None\n",
"\n",
" return None"
" def num_fit_clients(self, num_available_clients: int) -> Tuple[int, int]:\n",
" \"\"\"Return sample size and required number of clients.\"\"\"\n",
" num_clients = int(num_available_clients * self.fraction_fit)\n",
" return max(num_clients, self.min_fit_clients), self.min_available_clients\n",
"\n",
" def num_evaluation_clients(self, num_available_clients: int) -> Tuple[int, int]:\n",
" \"\"\"Use a fraction of available clients for evaluation.\"\"\"\n",
" num_clients = int(num_available_clients * self.fraction_evaluate)\n",
" return max(num_clients, self.min_evaluate_clients), self.min_available_clients"
]
},
{
Expand Down Expand Up @@ -447,7 +513,7 @@
"source": [
"## Recap\n",
"\n",
"[WIP - add description]"
"In this notebook, we’ve seen how to implement a custom strategy. A custom strategy enables granular control over client node configuration, result aggregation, and more. To define a custom strategy, you only have to overwrite the abstract methods of the (abstract) base class `Strategy`. To make custom strategies even more powerful, you can pass custom functions to the constructor of your new class (`__init__`) and then call these functions whenever needed. "
]
},
{
Expand All @@ -469,7 +535,7 @@
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "Flower-2-Strategies-in-FL-PyTorch.ipynb",
"name": "Flower-3-Building-a-Strategy-PyTorch.ipynb",
"provenance": [],
"toc_visible": true
},
Expand Down