Strategies¶

Federated Averaging¶

class substrafl.strategies.FedAvg(algo: algorithms.algo.Algo)¶

Bases: substrafl.strategies.strategy.Strategy

Federated averaging strategy.

Federated averaging is the simplest federating strategy. A round consists in performing a predefined number of forward/backward passes on each client, aggregating updates by computing their means and distributing the consensus update to all clients. In FedAvg, strategy is performed in a centralized way, where a single server or AggregationNode communicates with a number of clients TrainDataNode and TestDataNode.

Formally, if \(w_t\) denotes the parameters of the model at round \(t\), a single round consists in the following steps:

\[\Delta w_t^{k} = \mathcal{O}^k_t(w_t| X_t^k, y_t^k, m) \Delta w_t = \sum_{k=1}^K \frac{n_k}{n} \Delta w_t^k w_{t + 1} = w_t + \Delta w_t\]

where \(\mathcal{O}^k_t\) is the local optimizer algorithm of client \(k\) taking as argument the averaged weights as well as the \(t\)-th batch of data for local worker \(k\) and the number of local updates \(m\) to perform, and where \(n_k\) is the number of samples for worker \(k\), \(n = \sum_{k=1}^K n_k\) is the total number of samples.

Parameters: algo (Algo) – The algorithm your strategy will execute (i.e. train and test on all the specified nodes)

avg_shared_states(shared_states: Optional[List[substrafl.schemas.FedAvgSharedState]]) → substrafl.schemas.FedAvgAveragedState¶

Compute the weighted average of all elements returned by the train methods of the user-defined algorithm. The average is weighted by the proportion of the number of samples.

Example

shared_states = [
    {"weights": [3, 3, 3], "gradient": [4, 4, 4], "n_samples": 20},
    {"weights": [6, 6, 6], "gradient": [1, 1, 1], "n_samples": 40},
]
result = {"weights": [5, 5, 5], "gradient": [2, 2, 2]}

Parameters

shared_states (List[FedAvgSharedState]) – The list of the shared_state returned by the train method of the algorithm for each organization.

Raises

EmptySharedStatesError – The train method of your algorithm must return a shared_state
TypeError – Each shared_state must contains the key n_samples
TypeError – Each shared_state must contains at least one element to average
TypeError – All the elements of shared_states must be similar (same keys)
TypeError – All elements to average must be of type np.ndarray

Returns

A dict containing the weighted average of each input parameters without the passed key “n_samples”.

Return type

FedAvgAveragedState

property name: substrafl.schemas.StrategyName¶

The name of the strategy

Returns: Name of the strategy
Return type: StrategyName

perform_predict(test_data_nodes: List[substrafl.nodes.test_data_node.TestDataNode], train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], round_idx: int)¶

Perform prediction on test_data_nodes.

Parameters

test_data_nodes (List[TestDataNode]) – test data nodes to perform the prediction from the algo on.
train_data_nodes (List[TrainDataNode]) – train data nodes the model has been trained on.
round_idx (int) – round index.

perform_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], aggregation_node: organizations.aggregation_node.AggregationNode, round_idx: int, clean_models: bool, additional_orgs_permissions: Optional[set] = None)¶

One round of the Federated Averaging strategy consists in:

if round_idx==0: initialize the strategy by performing a local update
(train on n mini-batches) of the models on each train data node
aggregate the model shared_states
set the model weights to the aggregated weights on each train data nodes
perform a local update (train on n mini-batches) of the models on each train data nodes

Parameters

train_data_nodes (List[TrainDataNode]) – List of the nodes on which to perform local updates.
aggregation_node (AggregationNode) – Node without data, used to perform operations on the shared states of the models
round_idx (int) – Round number, it starts at 0.
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization.

Scaffold¶

class substrafl.strategies.Scaffold(algo: algorithms.algo.Algo, aggregation_lr: float = 1)¶

Bases: substrafl.strategies.strategy.Strategy

Scaffold strategy. Paper: https://arxiv.org/pdf/1910.06378.pdf Scaffold is Federated Averaging with control variates. By adding auxiliary variables in the clients and server the authors of the related paper prove better bounds on the convergence assuming certain hypothesis, in particular with non-iid data.

A round consists in performing a predefined number of forward/backward passes on each client, aggregating updates by computing their means and distributing the consensus update to all clients. In Scaffold, strategy is performed in a centralized way, where a single server or AggregationNode communicates with a number of clients TrainDataNode and TestDataNode.

Parameters

algo (Algo) – The algorithm your strategy will execute (i.e. train and test on all the specified nodes)
aggregation_lr (float, Optional) – Global aggregation rate applied on the averaged weight updates (eta_g in the paper). Defaults to 1. Must be >=0.

avg_shared_states(shared_states: Optional[List[substrafl.schemas.ScaffoldSharedState]]) → substrafl.schemas.ScaffoldAveragedStates¶

Performs the aggregation of the shared states returned by the train methods of the user-defined algorithm, according to the server operations of the Scaffold Algo.

Computes the weighted average of the weight updates and applies the aggregation learning rate
Updates the server control variate with the weighted average of the control variate updates

The average is weighted by the proportion of the number of samples.

Parameters: shared_states (List[ScaffoldSharedState]) – Shared state returned by the train method of the algorithm for each client (e.g. algorithms.pytorch.scaffold.train)
Returns: averaged weight updates and updated server control variate
Return type: ScaffoldAveragedStates

property name: substrafl.schemas.StrategyName¶

The name of the strategy

Returns: Name of the strategy
Return type: StrategyName

perform_predict(test_data_nodes: List[substrafl.nodes.test_data_node.TestDataNode], train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], round_idx: int)¶

Perform prediction on test_data_nodes.

Parameters

test_data_nodes (List[TestDataNode]) – test data nodes to perform the prediction from the algo on.
train_data_nodes (List[TrainDataNode]) – train data nodes the model has been trained on.
round_idx (int) – round index.

perform_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], aggregation_node: organizations.aggregation_node.AggregationNode, round_idx: int, clean_models: bool, additional_orgs_permissions: Optional[set] = None)¶

One round of the Scaffold strategy consists in:

if round_idx==0: initialize the strategy by performing a local update
(train on n mini-batches) of the models on each train data nodes
aggregate the model shared_states
set the model weights to the aggregated weights on each train data nodes
perform a local update (train on n mini-batches) of the models on each train data nodes

Parameters

train_data_nodes (List[TrainDataNode]) – List of the organizations on which to perform
aggregation_node (organizations.aggregation_node.AggregationNode) – Node without data, used to perform operations on the shared states of the models
round_idx (int) – Round number, it starts at 0.
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization.
aggregation_node –

Newton Raphson¶

class substrafl.strategies.NewtonRaphson(algo: algorithms.algo.Algo, damping_factor: float)¶

Bases: substrafl.strategies.strategy.Strategy

Newton-Raphson strategy.

Newton-Raphson strategy is based on Newton-Raphson distributed method. It leads to a faster divergence than a standard FedAvg strategy, however it can only be used on convex problems.

In a local step, the first order derivative (gradients) and the second order derivative (Hessian Matrix) of loss with respect to weights is calculated for each node.

Hessians and gradients are averaged on the aggregation node.

Updates of the weights are then calculated using the formula:

\[update = -\eta * H^{-1}_\theta.\Delta_\theta\]

Where \(H\) is the Hessian of the loss with respect to \(\theta\) and \(\Delta_\theta\) is the gradients of the loss with respect to \(\theta\) and \(0 < \eta <= 1\) is the damping factor.

Parameters

algo (Algo) – The algorithm your strategy will execute (i.e. train and test on all the specified nodes)
damping_factor (float) – Must be between 0 and 1. Multiplicative coefficient of the parameters update. Smaller value for \(\eta\) will increase the stability but decrease the speed of convergence of the gradient descent. Recommended value: damping_factor=0.8.

compute_averaged_states(shared_states: Optional[List[substrafl.schemas.NewtonRaphsonSharedState]]) → substrafl.schemas.NewtonRaphsonAveragedStates¶

Given the gradients and the Hessian (the second order derivative of loss with respect to weights), updates of the weights are calculated using the formula:

\[update = -\eta * H^{-1}_\theta.\Delta_\theta\]

Where \(H\) is the Hessian of the loss with respect to \(\theta\) and \(\Delta_\theta\) is the gradients of the loss with respect to \(\theta\) and \(0 < \eta <= 1\) is the damping factor.

The average is weighted by the number of samples.

Example

shared_states = [
    {"gradients": [1, 1, 1], "hessian": [[1, 0, 0], [0, 1, 0], [0, 0, 1]], "n_samples": 2},
    {"gradients": [2, 2, 2], "hessian": [[2, 0, 0], [0, 2, 0], [0, 0, 2]], "n_samples": 1}
]
damping_factor = 1

average = {"gradients": [4/3, 4/3, 4/3], "hessian": [[4/3, 0, 0],[0, 4/3, 0], [0, 0, 4/3]]}

Parameters

shared_states (List[NewtonRaphsonSharedState]) – The list of the shared_state returned by the train method of the algorithm for each node.

Raises

EmptySharedStatesError – The train method of your algorithm must return a shared_state
TypeError – Each shared_state must contains the key n_samples, gradients and hessian
TypeError – Each shared_state must contains at least one element to average
TypeError – All the elements of shared_states must be similar (same keys)
TypeError – All elements to average must be of type np.array

Returns

A dict containing the parameters updates of each input parameters.

Return type

NewtonRaphsonAveragedStates

property name: substrafl.schemas.StrategyName¶

The name of the strategy

Returns: Name of the strategy
Return type: StrategyName

perform_predict(test_data_nodes: List[substrafl.nodes.test_data_node.TestDataNode], train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], round_idx: int)¶

Perform prediction on test_data_nodes.

Parameters

test_data_nodes (List[TestDataNode]) – test data nodes to perform the prediction from the algo on.
train_data_nodes (List[TrainDataNode]) – train data nodes the model has been trained on.
round_idx (int) – round index.

perform_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], aggregation_node: organizations.aggregation_node.AggregationNode, round_idx: int, clean_models: bool, additional_orgs_permissions: Optional[set] = None)¶

One round of the Newton-Raphson strategy consists in:

if round_ids==0: initialize the strategy by performing a local update of the models on each train data nodes

aggregate the model shared_states

set the model weights to the aggregated weights on each train data nodes

perform a local update of the models on each train data nodes

Parameters

train_data_nodes (List[TrainDataNode]) – List of the nodes on which to perform local updates
aggregation_node (AggregationNode) – node without data, used to perform operations on the shared states of the models
round_idx (int) – Round number, it starts at 0.
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization.

Single Organization¶

class substrafl.strategies.SingleOrganization(algo: algorithms.algo.Algo)¶

Bases: substrafl.strategies.strategy.Strategy

Single organization strategy.

Single organization is not a real federated strategy and it is rather used for testing as it is faster than other ‘real’ strategies. The training and prediction are performed on a single Node. However, the number of passes to that Node (num_rounds) is still defined to test the actual federated setting. In SingleOrganization strategy a single client TrainDataNode and TestDataNode performs all the model execution.

Parameters: algo (Algo) – The algorithm your strategy will execute (i.e. train and test on all the specified nodes)

initialization_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], clean_models: bool, round_idx: Optional[int] = 0, additional_orgs_permissions: Optional[set] = None)¶

Call the initialize function of the algo on each train node.

Parameters

train_data_nodes (List[TrainDataNode]) – list of the train organizations
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
round_idx (Optional[int]) – index of the round. Defaults to 0.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization. Default to None

property name: substrafl.schemas.StrategyName¶

The name of the strategy

Returns: Name of the strategy
Return type: StrategyName

perform_predict(test_data_nodes: List[substrafl.nodes.test_data_node.TestDataNode], train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], round_idx: int)¶

Perform prediction on test_data_nodes.

Parameters

test_data_nodes (List[TestDataNode]) – test data nodes to perform the prediction from the algo on.
train_data_nodes (List[TrainDataNode]) – train data nodes the model has been trained on.
round_idx (int) – round index.

perform_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], round_idx: int, clean_models: bool, aggregation_node: Optional[organizations.aggregation_node.AggregationNode] = None, additional_orgs_permissions: Optional[set] = None)¶

One round of the SingleOrganization strategy: perform a local update (train on n mini-batches) of the models on a given data node

Parameters

train_data_nodes (List[TrainDataNode]) – List of the nodes on which to perform local updates, there should be exactly one item in the list.
aggregation_node (AggregationNode) – Should be None otherwise it will be ignored
round_idx (int) – Round number, it starts at 0.
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization.

Strategies Base Class¶

class substrafl.strategies.strategy.Strategy(algo: algorithms.algo.Algo, *args, **kwargs)¶

Bases: abc.ABC

Base strategy to be inherited from SubstraFL strategies.

All child class arguments need to be passed to it through its args and kwargs in order to use them when instantiating it as a RemoteStruct in each process.

Example

class MyStrat(Strategy):
    def __init__(self, algo, my_custom_arg):
        super.__init__(algo=algo, my_custom_arg=my_custom_arg)

Parameters: algo (Algo) – The algorithm your strategy will execute (i.e. train and test on all the specified nodes)
Raises: exceptions.IncompatibleAlgoStrategyError – Raise an error if the strategy name is not in algo.strategies.

build_graph(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], aggregation_node: Optional[List[organizations.aggregation_node.AggregationNode]], evaluation_strategy: Optional[substrafl.evaluation_strategy.EvaluationStrategy], num_rounds: int, clean_models: Optional[bool])¶

Build the computation graph of the strategy. The built graph will be stored by side effect in the given train_data_nodes, aggregation_nodes and evaluation_strategy. This function create a graph be first calling the initialization_round method of the strategy at round 0, and then call the perform_round method for each new round. If the current round is part of the evaluation strategy, the perform_predict method is called to complete the graph.

Parameters

train_data_nodes (List[TrainDataNode]) – list of the train organizations
aggregation_node (Optional[AggregationNode]) – aggregation node, necessary for centralized strategy, unused otherwise
evaluation_strategy (Optional[EvaluationStrategy]) – _description_
num_rounds (int) – _description_
clean_models (bool) – Clean the intermediary models on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed. Defaults to True.

initialization_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], clean_models: bool, round_idx: Optional[int] = 0, additional_orgs_permissions: Optional[set] = None)¶

Call the initialize function of the algo on each train node.

Parameters

train_data_nodes (List[TrainDataNode]) – list of the train organizations
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
round_idx (Optional[int]) – index of the round. Defaults to 0.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization. Default to None

abstract property name: substrafl.schemas.StrategyName¶

The name of the strategy

Returns: Name of the strategy
Return type: StrategyName

abstract perform_predict(test_data_nodes: List[substrafl.nodes.test_data_node.TestDataNode], train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], round_idx: int)¶

Perform the prediction of the algo on each test nodes. Gets the model for a train organization and compute the prediction on the test nodes.

Parameters

test_data_nodes (List[TestDataNode]) – list of nodes on which to evaluate
train_data_nodes (List[TrainDataNode]) – list of nodes on which the model has been trained
round_idx (int) – index of the round

abstract perform_round(train_data_nodes: List[substrafl.nodes.train_data_node.TrainDataNode], aggregation_node: Optional[organizations.aggregation_node.AggregationNode], round_idx: int, clean_models: bool, additional_orgs_permissions: Optional[set] = None)¶

Perform one round of the strategy

Parameters

train_data_nodes (List[TrainDataNode]) – list of the train organizations
aggregation_node (Optional[AggregationNode]) – aggregation node, necessary for centralized strategy, unused otherwise
round_idx (int) – index of the round
clean_models (bool) – Clean the intermediary models of this round on the Substra platform. Set it to False if you want to download or re-use intermediary models. This causes the disk space to fill quickly so should be set to True unless needed.
additional_orgs_permissions (Optional[set]) – Additional permissions to give to the model outputs after training, in order to test the model on an other organization.