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train_federated_server.py
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# -*- coding: utf-8 -*-
from flwr.server.client_proxy import ClientProxy
from flwr.common import (
EvaluateIns,
EvaluateRes,
FitIns,
FitRes,
MetricsAggregationFn,
NDArrays,
Parameters,
Scalar,
ndarrays_to_parameters,
parameters_to_ndarrays,
)
from datasets import Dataset, load_metric
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
get_scheduler
)
from typing import Callable, Dict, List, Optional, Tuple, Union
import flwr as fl
import torch
import numpy as np
import argparse
import os
from collections import OrderedDict
from save_load_util import save_metrics
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
def get_parser():
parser = argparse.ArgumentParser(description='set federated learning server argument')
parser.add_argument('-le', '--local-epoch', type=int, default=2,
help='the number times that the learning algorithm work through the entire training dataset, default 5')
parser.add_argument('-e', '--eval-time', type=int, default=2,
help='the number evaluate times in a epoch, default 2')
parser.add_argument('-b', '--batch-size', type=int, default=32,
help='batch size of training loader, default 32')
parser.add_argument('-l', '--learning-rate', type=float, default=0.001,
help='learning rate, default 0.001')
parser.add_argument('-r', '--num-round', type=int, default=2,
help='num of round of server aggregation, defaults to 2')
parser.add_argument('-ff', '--fraction-fit', type=float, default=1.0,
help='Fraction of clients used during training. Defaults to 1.0.')
parser.add_argument('-fe', '--fraction-evaluate', type=float, default=1.0,
help='Fraction of clients used during validation. Defaults to 1.0.')
parser.add_argument('-mf', '--min-fit-clients', type=int, default=2,
help='Minimum number of clients used during training. Defaults to 2.')
parser.add_argument('-me', '--min-evaluate-clients', type=int, default=2,
help='Minimum number of clients used during validation. Defaults to 2.')
parser.add_argument('-ma', '--min-available-clients', type=int, default=2,
help='Minimum number of total clients in the system. Defaults to 2.')
parser.add_argument('-mds', '--model-saving-directory', type=str, default='./server_model',
help='specify working directory to save model, default ./server_model')
parser.add_argument('-mts', '--metrics-saving-directory', type=str, default='.',
help='specify working directory to save metrics files, default current')
parser.add_argument('-ba', '--best-valid-accuracy', type=float, default=0.0,
help='specify the minimum validation accuracy, default 0.0')
return parser
local_config = None # define global server config set by main argument
metrics_dict = {
'train_acc_list': [],
'valid_acc_list': [],
'train_loss_list': [],
'valid_loss_list': []
}
def get_on_fit_config_fn(args):
'''Return a function which returns training configurations.'''
def fit_config(server_round: int):
'''Return a configuration with static batch size and (local) epochs.'''
config = {
'current_round': server_round,
'learning_rate': args.learning_rate,
'batch_size': args.batch_size,
'eval_time': args.eval_time,
'local_epochs': args.local_epoch
}
return config
return fit_config
def get_on_evaluate_config_fn(args):
'''Return a function which returns testing configurations.'''
def evaluate_config(server_round: int):
'''Return a configuration with static batch size and (local) epochs.'''
config = {
'current_round': server_round,
'batch_size': args.batch_size
}
return config
return evaluate_config
def fit_metrics_aggregation_fn(fit_metrics):
total_data_size = sum(data_size for data_size, _ in fit_metrics)
factors = [data_size / total_data_size for data_size, _ in fit_metrics]
# metrics[0]為client data size，metrics[1]為client train accuracy值及valid accuracy值的dict
agg_train_acc = sum(metrics[1]['train_accuracy'] * factor for metrics, factor in zip(fit_metrics, factors))
agg_valid_acc = sum(metrics[1]['valid_accuracy'] * factor for metrics, factor in zip(fit_metrics, factors))
agg_train_loss = sum(metrics[1]['train_loss'] * factor for metrics, factor in zip(fit_metrics, factors))
agg_valid_loss = sum(metrics[1]['valid_loss'] * factor for metrics, factor in zip(fit_metrics, factors))
metrics_dict['train_acc_list'].append(agg_train_acc)
metrics_dict['valid_acc_list'].append(agg_valid_acc)
metrics_dict['train_loss_list'].append(agg_train_loss)
metrics_dict['valid_loss_list'].append(agg_valid_loss)
return {'train_accuracy': agg_train_acc,
'valid_accuracy': agg_valid_acc,
'train_loss': agg_train_loss,
'valid_loss': agg_valid_loss}
def evaluate_metrics_aggregation_fn(eval_metrics):
total_data_size = sum(data_size for data_size, _ in eval_metrics)
factors = [data_size / total_data_size for data_size, _ in eval_metrics]
# metrics[0]為client data size，metrics[1]為client testing loss值及accuracy的dict
agg_acc = sum(metrics[1]['accuracy'] * factor for metrics, factor in zip(eval_metrics, factors))
agg_loss = sum(metrics[1]['loss'] * factor for metrics, factor in zip(eval_metrics, factors))
return {'accuracy': agg_acc,
'loss': agg_loss}
def evaluate_fn(server_round, parameters, config):
model = AutoModelForSequenceClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=6,
id2label={0:'sadness', 1:'joy', 2:'love', 3:'anger', 4:'fear', 5:'surprise'},
label2id={'sadness':0, 'joy':1, 'love':2, 'anger':3, 'fear':4, 'surprise':5}
).to(device)
params_dict = zip(model.state_dict().keys(), parameters)
state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
if len(metrics_dict['valid_acc_list']) > 0 and \
local_config['best_valid_acc'] < metrics_dict['valid_acc_list'][-1]:
local_config['best_valid_acc'] = metrics_dict['valid_acc_list'][-1]
model.load_state_dict(state_dict, strict=True)
model.save_pretrained(local_config['model_saving_directory'])
print(f"Model saved to ==> {local_config['model_saving_directory']}")
if server_round == local_config['num_round']:
save_metrics(local_config['metrics_saving_directory'] + '/server_accuracy_metrics.pt',
metrics_dict['train_acc_list'],
metrics_dict['valid_acc_list'],
list(range(1, server_round + 1)))
save_metrics(local_config['metrics_saving_directory'] + '/server_loss_metrics.pt',
metrics_dict['train_loss_list'],
metrics_dict['valid_loss_list'],
list(range(1, server_round + 1)))
def main(args):
global local_config
local_config = {
'model_saving_directory': args.model_saving_directory,
'metrics_saving_directory': args.metrics_saving_directory,
'num_round': args.num_round,
'best_valid_acc': args.best_valid_accuracy
}
initial_parameters = None
if os.path.exists(local_config['model_saving_directory']):
model = AutoModelForSequenceClassification.from_pretrained(
args.model_saving_directory,
num_labels=6,
id2label={0:'sadness', 1:'joy', 2:'love', 3:'anger', 4:'fear', 5:'surprise'},
label2id={'sadness':0, 'joy':1, 'love':2, 'anger':3, 'fear':4, 'surprise':5}
).to(device)
weights = [val.cpu().numpy() for name, val in model.state_dict().items()]
initial_parameters = ndarrays_to_parameters(weights)
# Define strategy
strategy = fl.server.strategy.FedAvg(
initial_parameters=initial_parameters,
fraction_fit=args.fraction_fit,
fraction_evaluate=args.fraction_evaluate,
min_fit_clients=args.min_fit_clients,
min_evaluate_clients=args.min_evaluate_clients,
min_available_clients=args.min_available_clients,
on_fit_config_fn=get_on_fit_config_fn(args),
on_evaluate_config_fn=get_on_evaluate_config_fn(args),
evaluate_fn=evaluate_fn,
fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn
)
# Start server
fl.server.start_server(
server_address="[::1]:9999",
config=fl.server.ServerConfig(num_rounds=args.num_round),
strategy=strategy
)
if __name__ == '__main__':
parser = get_parser()
args = parser.parse_args()
main(args)