.. # Copyright (C) 2020-2023 Intel Corporation .. # SPDX-License-Identifier: Apache-2.0 .. _overriding_agg_fn: ***************************** Override Aggregation Function ***************************** With the aggregator-based workflow, you can use custom aggregation functions for each task via command line interface. Command Line Interface ====================== Predefined Aggregation Functions -------------------------------- Choose from the following predefined aggregation functions: - ``openfl.interface.aggregation_functions.WeightedAverage`` (default) - ``openfl.interface.aggregation_functions.Median`` - ``openfl.interface.aggregation_functions.GeometricMedian`` - ``openfl.interface.aggregation_functions.AdagradAdaptiveAggregation`` - ``openfl.interface.aggregation_functions.AdamAdaptiveAggregation`` - ``openfl.interface.aggregation_functions.YogiAdaptiveAggregation`` Custom Aggregation Functions ---------------------------- OpenFL provides interfaces to support your own custom aggregation functions. You can also create your own implementation of :class:`openfl.interface.aggregation_functions.core.AggregationFunction`. See `example `_ for details. 1. Define the behavior of the aggregation. 2. Include the implementation in the **plan.yaml** file in the **plan** directory of your workspace. 3. In the **tasks** section, pick a task for which you want to change the aggregation and insert ``aggregation_type`` section with a single ``template`` key that defines a module path to your class. The following is an example of a **plan.yaml** with a modified aggregation function: .. code-block:: yaml # ... # other top-level sections # ... tasks: aggregated_model_validation: function: validate kwargs: apply: global metrics: - acc defaults: plan/defaults/tasks_torch.yaml locally_tuned_model_validation: function: validate kwargs: apply: local metrics: - acc settings: {} train: function: train_batches aggregation_type: template: openfl.interface.aggregation_functions.Median kwargs: metrics: - loss Example of a Custom Aggregation Function ======================================== This is an example of a custom tensor clipping aggregation function that multiplies all local tensors by 0.3 and averages them according to weights equal to data parts to produce the resulting global tensor. .. code-block:: python from openfl.interface.aggregation_functions import AggregationFunction import numpy as np class ClippedAveraging(AggregationFunction): def __init__(self, ratio): self.ratio = ratio def call(self, local_tensors, db_iterator, tensor_name, fl_round, *__): """Aggregate tensors. Args: local_tensors(list[openfl.utilities.LocalTensor]): List of local tensors to aggregate. db_iterator: iterator over history of all tensors. Columns: - 'tensor_name': name of the tensor. Examples for `torch.nn.Module`s: 'conv1.weight', 'fc2.bias'. - 'round': 0-based number of round corresponding to this tensor. - 'tags': tuple of tensor tags. Tags that can appear: - 'model' indicates that the tensor is a model parameter. - 'trained' indicates that tensor is a part of a training result. These tensors are passed to the aggregator node after local learning. - 'aggregated' indicates that tensor is a result of aggregation. These tensors are sent to collaborators for the next round. - 'delta' indicates that value is a difference between rounds for a specific tensor. also one of the tags is a collaborator name if it corresponds to a result of a local task. - 'nparray': value of the tensor. tensor_name: name of the tensor fl_round: round number tags: tuple of tags for this tensor Returns: np.ndarray: aggregated tensor """ clipped_tensors = [] previous_tensor_value = None for record in db_iterator: if ( record['round'] == (fl_round - 1) and record['tensor_name'] == tensor_name and 'aggregated' in record['tags'] and 'delta' not in record['tags'] ): previous_tensor_value = record['nparray'] weights = [] for local_tensor in local_tensors: prev_tensor = previous_tensor_value if previous_tensor_value is not None else local_tensor.tensor delta = local_tensor.tensor - prev_tensor new_tensor = prev_tensor + delta * self.ratio clipped_tensors.append(new_tensor) weights.append(local_tensor.weight) return np.average(clipped_tensors, weights=weights, axis=0) A full implementation can be found at `Federated_Pytorch_MNIST_custom_aggregation_Tutorial.ipynb `_ Example of a Privileged Aggregation Function ============================================ Most of the time the AggregationFunction interface is sufficient to implement custom methods, but in certain scenarios users may want to store additional information inside the TensorDB Dataframe beyond the aggregated tensor. The :class:`openfl.interface.aggregation_functions.experimental.PrivilegedAggregationFunction` interface is provided for this use, and gives the user direct access to aggregator's TensorDB dataframe (notice the `tensor_db` param in the call function replaces the `db_iterator` from the standard AggregationFunction interface). As the name suggests, this interface is called privileged because with great power comes great responsibility, and modifying the TensorDB dataframe directly can lead to unexpected behavior and experiment failures if entries are arbitrarily deleted. .. code-block:: python from openfl.interface.aggregation_functions.experimental import PrivilegedAggregationFunction import numpy as np import pandas as pd class PrioritizeLeastImproved(PrivilegedAggregationFunction): """ Give collaborator with the least improvement in validation accuracy more influence over future weights """ def call(self, local_tensors, tensor_db, tensor_name, fl_round, tags): """Aggregate tensors. Args: local_tensors(list[openfl.utilities.LocalTensor]): List of local tensors to aggregate. tensor_db: Aggregator's TensorDB [writable]. Columns: - 'tensor_name': name of the tensor. Examples for `torch.nn.Module`s: 'conv1.weight', 'fc2.bias'. - 'round': 0-based number of round corresponding to this tensor. - 'tags': tuple of tensor tags. Tags that can appear: - 'model' indicates that the tensor is a model parameter. - 'trained' indicates that tensor is a part of a training result. These tensors are passed to the aggregator node after local learning. - 'aggregated' indicates that tensor is a result of aggregation. These tensors are sent to collaborators for the next round. - 'delta' indicates that value is a difference between rounds for a specific tensor. also one of the tags is a collaborator name if it corresponds to a result of a local task. - 'nparray': value of the tensor. tensor_name: name of the tensor fl_round: round number tags: tuple of tags for this tensor Returns: np.ndarray: aggregated tensor """ from openfl.utilities import change_tags tensors, weights, collaborators = zip(*[(x.tensor, x.weight, x.col_name) for idx,x in enumerate(local_tensors)]) tensors, weights, collaborators = np.array(tensors), np.array(weights), collaborators if fl_round > 0: metric_tags = ('metric','validate_agg') collaborator_accuracy = {} previous_col_accuracy = {} change_in_accuracy = {} for col in collaborators: col_metric_tag = change_tags(metric_tags,add_field=col) collaborator_accuracy[col] = float(tensor_db[(tensor_db['tensor_name'] == 'acc') & (tensor_db['round'] == fl_round) & (tensor_db['tags'] == col_metric_tag)]['nparray']) previous_col_accuracy[col] = float(tensor_db[(tensor_db['tensor_name'] == 'acc') & (tensor_db['round'] == fl_round - 1) & (tensor_db['tags'] == col_metric_tag)]['nparray']) change_in_accuracy[col] = collaborator_accuracy[col] - previous_col_accuracy[col] least_improved_collaborator = min(change_in_accuracy,key=change_in_accuracy.get) # Dont add least improved collaborator more than once if len(tensor_db[(tensor_db['tags'] == ('least_improved',)) & (tensor_db['round'] == fl_round)]) == 0: tensor_db.loc[tensor_db.shape[0]] = \ ['_','_',fl_round,True,('least_improved',),np.array(least_improved_collaborator)] fx.logger.info(f'Least improved collaborator = {least_improved_collaborator}') fx.logger.info(f"Least improved = {tensor_db[(tensor_db['tags'] == ('least_improved',)) & (tensor_db['nparray'] == np.array(least_improved_collaborator))]}") fx.logger.info(f'Collaborator accuracy = {collaborator_accuracy}') fx.logger.info(f'Change in accuracy {change_in_accuracy}') least_improved_weight_factor = 0.1 * len(tensor_db[(tensor_db['tags'] == ('least_improved',)) & (tensor_db['nparray'] == np.array(least_improved_collaborator))]) weights[collaborators.index(least_improved_collaborator)] += least_improved_weight_factor weights = weights / np.sum(weights) return np.average(tensors, weights=weights, axis=0) A full implementation can be found at `Federated_Pytorch_MNIST_custom_aggregation_Tutorial.ipynb `_