Override Aggregation Function

With the aggregator-based workflow, you can use custom aggregation functions for each task via Python* API or command line interface.

Python API

1. Create an implementation of openfl.interface.aggregation_functions.core.AggregationFunction.

2. In the override_config keyword argument of the openfl.native.run_experiment() native function, pass the implementation as a tasks.{task_name}.aggregation_type parameter.

Note

See Federated PyTorch MNIST Tutorial for an example of the custom aggregation function.

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

Adaptive Aggregation Functions

Note

To create adaptive aggregation functions, the user must specify parameters for the aggregation optimizer (NumPyAdagrad, NumPyAdam or NumPyYogi) that will aggregate the global model. Theese parameters parameters are passed via keywords.

Also, user must pass one of the arguments: params - model parameters (a dictionary with named model parameters in the form of numpy arrays), or pass model_interface - an instance of the ModelInterface class. If user pass both params and model_interface, then the optimizer parameters are initialized via params, ignoring model_interface argument.

See the AdagradAdaptiveAggregation definitions for details.

Adaptive federated optimization original paper.

AdagradAdaptiveAggregation usage example:

from openfl.interface.interactive_api.experiment import TaskInterface, ModelInterface
from openfl.interface.aggregation_functions import AdagradAdaptiveAggregation

TI = TaskInterface()
MI = ModelInterface(model=model,
                    optimizer=optimizer,
                    framework_plugin=framework_adapter)
...

# Creating aggregation function
agg_fn = AdagradAdaptiveAggregation(model_interface=MI,
                                    learning_rate=0.4)

# Define training task
@TI.register_fl_task(model='model', data_loader='train_loader', \
                        device='device', optimizer='optimizer')
@TI.set_aggregation_function(agg_fn)
def train(...):
...

You can define your own numpy based optimizer, which will be used for global model aggreagation:

from openfl.utilities.optimizers.numpy.base_optimizer import Optimizer

class MyOpt(Optimizer):
    """My optimizer implementation."""

    def __init__(
        self,
        *,
        params: Optional[Dict[str, np.ndarray]] = None,
        model_interface=None,
        learning_rate: float = 0.001,
        param1: Any = None,
        param2: Any = None
    ) -> None:
        """Initialize.

        Args:
            params: Parameters to be stored for optimization.
            model_interface: Model interface instance to provide parameters.
            learning_rate: Tuning parameter that determines
                the step size at each iteration.
            param1: My own defined parameter.
            param2: My own defined parameter.
        """
        super().__init__()
        pass # Your code here!

    def step(self, gradients: Dict[str, np.ndarray]) -> None:
        """
        Perform a single step for parameter update.

        Implement your own optimizer weights update rule.

        Args:
            gradients: Partial derivatives with respect to optimized parameters.
        """
        pass # Your code here!
...

from openfl.interface.aggregation_functions import WeightedAverage
from openfl.interface.aggregation_functions.core import AdaptiveAggregation

# Creating your implemented optimizer instance based on numpy:
my_own_optimizer = MyOpt(model_interface=MI, learning_rate=0.01)

# Creating aggregation function
agg_fn = AdaptiveAggregation(optimizer=my_own_optimizer,
                             agg_func=WeightedAverage()) # WeightedAverage() is used for aggregating
                                                         # parameters that are not inside the given optimizer.

# Define training task
@TI.register_fl_task(model='model', data_loader='train_loader', \
                        device='device', optimizer='optimizer')
@TI.set_aggregation_function(agg_fn)
def train(...):
...

Note

If you do not understand how to write your own numpy based optimizer, please see the NumPyAdagrad and AdaptiveAggregation definitions for details.

Custom Aggregation Functions

OpenFL provides interfaces to support your own custom aggregation functions. You can also create your own implementation of 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:

# ...
# 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

Interactive API

You can override aggregation function that will be used for the task this function corresponds to. In order to do this, call the set_aggregation_function decorator method of TaskInterface and pass AggregationFunction subclass instance as a parameter. For example, you can try:

from openfl.interface.aggregation_functions import Median
TI = TaskInterface()
agg_fn = Median()
@TI.register_fl_task(model='model', data_loader='train_loader', \
                     device='device', optimizer='optimizer')
@TI.set_aggregation_function(agg_fn)

Warning

All tasks with the same type of aggregation use the same class instance. If AggregationFunction implementation has its own state, then this state will be shared across tasks.

AggregationFunction requires a single call function. This function receives tensors for a single parameter from multiple collaborators with additional metadata (see definition of openfl.interface.aggregation_functions.core.AggregationFunction.call()) and returns a single tensor that represents the result of aggregation.

Note

See the definition of openfl.interface.aggregation_functions.core.AggregationFunction.call for details.

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.

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 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.

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