Source code for fedsim.fl.algorithms.feddyn

r""" This file contains an implementation of the following paper:
    Title: "Federated Learning Based on Dynamic Regularization"
    Authors: Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, Venkatesh Saligrama
    Publication date: [28 Sept 2020 (modified: 25 Mar 2021)]
    Link: https://openreview.net/forum?id=B7v4QMR6Z9w
"""
from torch.nn.utils import parameters_to_vector
from functools import partial

import torch

from . import fedavg
from ..utils import vector_to_parameters_like, default_closure


[docs]class FedDyn(fedavg.FedAvg): def __init__( self, data_manager, metric_logger, num_clients, sample_scheme, sample_rate, model_class, epochs, loss_fn, batch_size=32, test_batch_size=64, local_weight_decay=0., slr=1., clr=0.1, clr_decay=1., clr_decay_type='step', min_clr=1e-12, clr_step_size=1000, device='cuda', log_freq=10, mu=0.02, *args, **kwargs, ): self.mu = mu super(FedDyn, self).__init__( data_manager, metric_logger, num_clients, sample_scheme, sample_rate, model_class, epochs, loss_fn, batch_size, test_batch_size, local_weight_decay, slr, clr, clr_decay, clr_decay_type, min_clr, clr_step_size, device, log_freq, ) cloud_params = self.read_server('cloud_params') self.write_server('avg_params', cloud_params.detach().clone()) self.write_server('h', torch.zeros_like(cloud_params)) for client_id in range(num_clients): self.write_client(client_id, 'h', torch.zeros_like(cloud_params)) # oracle read violation, num_clients read violation average_sample = len(self.oracle_dataset['train']) / self.num_clients self.write_server('average_sample', average_sample)
[docs] def send_to_server( self, client_id, datasets, epochs, loss_fn, batch_size, lr, weight_decay=0, device='cuda', ctx=None, *args, **kwargs, ): model = ctx['model'] params_init = parameters_to_vector(model.parameters()).detach().clone() h = self.read_client(client_id, 'h') mu_adaptive = self.mu / len(datasets['train']) *\ self.read_server('average_sample') def transform_grads_fn(model): params = parameters_to_vector(model.parameters()) grad_additive = 0.5 * (params - params_init) - h grad_additive_list = vector_to_parameters_like( mu_adaptive * grad_additive, model.parameters()) for p, g_a in zip(model.parameters(), grad_additive_list): p.grad += g_a step_closure_ = partial(default_closure, transform_grads=transform_grads_fn) opt_res = super(FedDyn, self).send_to_server( client_id, datasets, epochs, loss_fn, batch_size, lr, weight_decay, device, ctx, step_closure=step_closure_, *args, **kwargs, ) # update local h pseudo_grads = ( params_init - \ parameters_to_vector(model.parameters()).detach().clone().data ) new_h = h + pseudo_grads self.write_client(client_id, 'h', new_h) return opt_res
[docs] def receive_from_client(self, client_id, client_msg, aggregation_results): weight = 1 self.agg(client_id, client_msg, aggregation_results, weight=weight)
[docs] def optimize(self, aggregator): if 'local_params' in aggregator: weight = aggregator.get_weight('local_params') param_avg = aggregator.pop('local_params') optimizer = self.read_server('optimizer') cloud_params = self.read_server('cloud_params') pseudo_grads = cloud_params.data - param_avg h = self.read_server('h') # read total clients VIOLATION h = h + weight / self.num_clients * pseudo_grads new_params = param_avg - h modified_pseudo_grads = cloud_params.data - new_params # update cloud params optimizer.zero_grad() cloud_params.grad = modified_pseudo_grads optimizer.step() self.write_server('avg_params', param_avg.detach().clone()) self.write_server('h', h.data) # purge aggregated results del param_avg return aggregator.pop_all()
[docs] def deploy(self): return dict( cloud=self.read_server('cloud_params'), avg=self.read_server('avg_params'), )