-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathpfedgraph_approx.py
163 lines (130 loc) · 6.93 KB
/
pfedgraph_approx.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
import copy
import math
import random
import time
from test import compute_acc, compute_local_test_accuracy
import numpy as np
import torch
import torch.optim as optim
from pfedgraph_approx.config import get_args
from pfedgraph_approx.utils import aggregation_by_graph, update_graph_matrix_neighbor
from model import simplecnn, textcnn
from prepare_data import get_dataloader
from attack import *
def local_train_pfedgraph(args, round, nets_this_round, cluster_models, train_local_dls, val_local_dls, test_dl, data_distributions, best_val_acc_list, best_test_acc_list, benign_client_list):
for net_id, net in nets_this_round.items():
train_local_dl = train_local_dls[net_id]
data_distribution = data_distributions[net_id]
cluster_model = cluster_models[net_id]
val_acc = compute_acc(net, val_local_dls[net_id])
personalized_test_acc, generalized_test_acc = compute_local_test_accuracy(net, test_dl, data_distribution)
if val_acc > best_val_acc_list[net_id]:
best_val_acc_list[net_id] = val_acc
best_test_acc_list[net_id] = personalized_test_acc
print('>> Client {} test 1 | (Pre) Personalized Test Acc: ({:.5f}) | Generalized Test Acc: {:.5f}'.format(net_id, personalized_test_acc, generalized_test_acc))
# Set Optimizer
if args.optimizer == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr, weight_decay=args.reg)
elif args.optimizer == 'amsgrad':
optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr, weight_decay=args.reg,
amsgrad=True)
elif args.optimizer == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr, momentum=0.9, weight_decay=args.reg)
criterion = torch.nn.CrossEntropyLoss()
cluster_model.cuda()
flatten_cluster_model = []
for param in cluster_model.parameters():
flatten_cluster_model.append(param.reshape(-1))
flatten_cluster_model = torch.cat(flatten_cluster_model)
net.cuda()
net.train()
iterator = iter(train_local_dl)
for iteration in range(args.num_local_iterations):
try:
x, target = next(iterator)
except StopIteration:
iterator = iter(train_local_dl)
x, target = next(iterator)
x, target = x.cuda(), target.cuda()
optimizer.zero_grad()
target = target.long()
out = net(x)
loss = criterion(out, target)
if round > 0:
flatten_model = []
for param in net.parameters():
flatten_model.append(param.reshape(-1))
flatten_model = torch.cat(flatten_model)
loss2 = args.lam * torch.nn.functional.cosine_similarity(flatten_cluster_model.unsqueeze(0), flatten_model.unsqueeze(0))
loss2.backward()
loss.backward()
optimizer.step()
if net_id in benign_client_list:
val_acc = compute_acc(net, val_local_dls[net_id])
personalized_test_acc, generalized_test_acc = compute_local_test_accuracy(net, test_dl, data_distribution)
if val_acc > best_val_acc_list[net_id]:
best_val_acc_list[net_id] = val_acc
best_test_acc_list[net_id] = personalized_test_acc
print('>> Client {} | Personalized Test Acc: {:.5f} | Generalized Test Acc: {:.5f}'.format(net_id, personalized_test_acc, generalized_test_acc))
net.to('cpu')
cluster_model.to('cpu')
return np.array(best_test_acc_list)[np.array(benign_client_list)].mean()
args, cfg = get_args()
print(args)
seed = args.init_seed
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
random.seed(seed)
n_party_per_round = int(args.n_parties * args.sample_fraction)
party_list = [i for i in range(args.n_parties)]
party_list_rounds = []
if n_party_per_round != args.n_parties:
for i in range(args.comm_round):
party_list_rounds.append(random.sample(party_list, n_party_per_round))
else:
for i in range(args.comm_round):
party_list_rounds.append(party_list)
benign_client_list = random.sample(party_list, int(args.n_parties * (1-args.attack_ratio)))
benign_client_list.sort()
print(f'>> -------- Benign clients: {benign_client_list} --------')
train_local_dls, val_local_dls, test_dl, net_dataidx_map, traindata_cls_counts, data_distributions = get_dataloader(args)
if args.dataset == 'cifar10':
model = simplecnn
elif args.dataset == 'cifar100':
model = simplecnn
elif args.dataset == 'yahoo_answers':
model = textcnn
global_model = model(cfg['classes_size'])
global_parameters = global_model.state_dict()
local_models = []
cluster_models = []
best_val_acc_list, best_test_acc_list = [],[]
dw = []
for i in range(cfg['client_num']):
local_models.append(model(cfg['classes_size']))
cluster_models.append(model(cfg['classes_size']))
dw.append({key : torch.zeros_like(value) for key, value in local_models[i].named_parameters()})
best_val_acc_list.append(0)
best_test_acc_list.append(0)
graph_matrix = torch.ones(len(local_models), len(local_models)) / (len(local_models)-1) # Collaboration Graph
graph_matrix[range(len(local_models)), range(len(local_models))] = 0
for net in local_models:
net.load_state_dict(global_parameters)
for net in cluster_models:
net.load_state_dict(global_parameters)
for round in range(cfg["comm_round"]):
party_list_this_round = party_list_rounds[round]
if args.sample_fraction < 1.0:
print(f'>> Clients in this round : {party_list_this_round}')
nets_this_round = {k: local_models[k] for k in party_list_this_round}
nets_param_start = {k: copy.deepcopy(local_models[k]) for k in party_list_this_round}
mean_personalized_acc = local_train_pfedgraph(args, round, nets_this_round, cluster_models, train_local_dls, val_local_dls, test_dl, data_distributions, best_val_acc_list, best_test_acc_list, benign_client_list)
total_data_points = sum([len(net_dataidx_map[k]) for k in party_list_this_round])
fed_avg_freqs = {k: len(net_dataidx_map[k]) / total_data_points for k in party_list_this_round}
manipulate_gradient(args, None, nets_this_round, benign_client_list, nets_param_start)
graph_matrix = update_graph_matrix_neighbor(graph_matrix, nets_this_round, global_parameters, dw, fed_avg_freqs, args.alpha, args.difference_measure) # Graph Matrix is not normalized yet
aggregation_by_graph(cfg, graph_matrix, nets_this_round, global_parameters, cluster_models) # Aggregation weight is normalized here
print('>> (Current) Round {} | Local Per: {:.5f}'.format(round, mean_personalized_acc))
print('-'*80)