Source code for fedsim.fl.algorithms.fedavgm

r""" This file contains an implementation of the following paper:
    Title: "Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification"
    Authors: Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown
    Publication date: September 13th, 2019
    Link: https://arxiv.org/abs/1909.06335
"""
from . import fedavg
from torch.optim import SGD


[docs]class FedAvgM(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, momentum=0.9, *args, **kwargs, ): self.momentum = momentum super(FedAvgM, 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, ) # over write optimizer params = self.read_server('cloud_params') optimizer = SGD(params=[params], lr=slr, momentum=self.momentum) self.write_server('optimizer', optimizer)