Source code for fedjax.algorithms.fed_avg

# Copyright 2021 Google LLC
#
# 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,
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"""Federated averaging implementation using fedjax.core.

This is the more performant implementation that matches what would be used in
the :py:mod:`fedjax.algorithms.fed_avg` . The key difference between this and
the basic version is the use of :py:mod:`fedjax.core.for_each_client`

Communication-Efficient Learning of Deep Networks from Decentralized Data
    H. Brendan McMahan, Eider Moore, Daniel Ramage,
    Seth Hampson, Blaise Aguera y Arcas. AISTATS 2017.
    https://arxiv.org/abs/1602.05629

Adaptive Federated Optimization
    Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush,
    Jakub Konečný, Sanjiv Kumar, H. Brendan McMahan. ICLR 2021.
    https://arxiv.org/abs/2003.00295
"""

from typing import Any, Callable, Mapping, Sequence, Tuple

from fedjax.core import client_datasets
from fedjax.core import dataclasses
from fedjax.core import federated_algorithm
from fedjax.core import federated_data
from fedjax.core import for_each_client
from fedjax.core import optimizers
from fedjax.core import tree_util
from fedjax.core.typing import BatchExample
from fedjax.core.typing import Params
from fedjax.core.typing import PRNGKey

import jax

Grads = Params


def create_train_for_each_client(grad_fn, client_optimizer):
  """Builds client_init, client_step, client_final for for_each_client."""

  def client_init(server_params, client_rng):
    opt_state = client_optimizer.init(server_params)
    client_step_state = {
        'params': server_params,
        'opt_state': opt_state,
        'rng': client_rng,
    }
    return client_step_state

  def client_step(client_step_state, batch):
    rng, use_rng = jax.random.split(client_step_state['rng'])
    grads = grad_fn(client_step_state['params'], batch, use_rng)
    opt_state, params = client_optimizer.apply(grads,
                                               client_step_state['opt_state'],
                                               client_step_state['params'])
    next_client_step_state = {
        'params': params,
        'opt_state': opt_state,
        'rng': rng,
    }
    return next_client_step_state

  def client_final(server_params, client_step_state):
    delta_params = jax.tree_util.tree_multimap(lambda a, b: a - b,
                                               server_params,
                                               client_step_state['params'])
    return delta_params

  return for_each_client.for_each_client(client_init, client_step, client_final)


[docs]@dataclasses.dataclass class ServerState: """State of server passed between rounds. Attributes: params: A pytree representing the server model parameters. opt_state: A pytree representing the server optimizer state. """ params: Params opt_state: optimizers.OptState
[docs]def federated_averaging( grad_fn: Callable[[Params, BatchExample, PRNGKey], Grads], client_optimizer: optimizers.Optimizer, server_optimizer: optimizers.Optimizer, client_batch_hparams: client_datasets.ShuffleRepeatBatchHParams ) -> federated_algorithm.FederatedAlgorithm: """Builds federated averaging. Args: grad_fn: A function from (params, batch_example, rng) to gradients. This can be created with :func:`fedjax.core.model.model_grad`. client_optimizer: Optimizer for local client training. server_optimizer: Optimizer for server update. client_batch_hparams: Hyperparameters for batching client dataset for train. Returns: FederatedAlgorithm """ train_for_each_client = create_train_for_each_client(grad_fn, client_optimizer) def init(params: Params) -> ServerState: opt_state = server_optimizer.init(params) return ServerState(params, opt_state) def apply( server_state: ServerState, clients: Sequence[Tuple[federated_data.ClientId, client_datasets.ClientDataset, PRNGKey]] ) -> Tuple[ServerState, Mapping[federated_data.ClientId, Any]]: client_num_examples = {cid: len(cds) for cid, cds, _ in clients} batch_clients = [(cid, cds.shuffle_repeat_batch(client_batch_hparams), crng) for cid, cds, crng in clients] client_diagnostics = {} # Running weighted mean of client updates. We do this iteratively to avoid # loading all the client outputs into memory since they can be prohibitively # large depending on the model parameters size. delta_params_sum = tree_util.tree_zeros_like(server_state.params) num_examples_sum = 0. for client_id, delta_params in train_for_each_client( server_state.params, batch_clients): num_examples = client_num_examples[client_id] delta_params_sum = tree_util.tree_add( delta_params_sum, tree_util.tree_weight(delta_params, num_examples)) num_examples_sum += num_examples # We record the l2 norm of client updates as an example, but it is not # required for the algorithm. client_diagnostics[client_id] = { 'delta_l2_norm': tree_util.tree_l2_norm(delta_params) } mean_delta_params = tree_util.tree_inverse_weight(delta_params_sum, num_examples_sum) server_state = server_update(server_state, mean_delta_params) return server_state, client_diagnostics def server_update(server_state, mean_delta_params): opt_state, params = server_optimizer.apply(mean_delta_params, server_state.opt_state, server_state.params) return ServerState(params, opt_state) return federated_algorithm.FederatedAlgorithm(init, apply)