def __init__(self, params, learner, dataset):
# transfer parameters to self
for key, val in params.items(): setattr(self, key, val);
self.parameters = params
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(*params['model_params'], self.inner_opt, self.seed)
#initilzation of clients
self.clients = self.setup_clients(dataset, self.client_model)
self.N_clients = len(self.clients)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = self.client_model.get_params()
# initialize system metrics
self.metrics = Metrics(self.clients, params)
self.rs_train_acc, self.rs_train_loss, self.rs_glob_acc = [], [], []
self.global_data_test = [] # generalization of global test accuracy
self.global_data_train = [] # specialization of global train accuracy
self.cs_data_test = np.zeros((self.num_rounds, self.N_clients))
self.cs_data_train = np.zeros((self.num_rounds, self.N_clients))
self.cg_data_test = np.zeros((self.num_rounds, self.N_clients))
self.cg_data_train = np.zeros((self.num_rounds, self.N_clients))
self.cg_avg_data_test = [] # avg generalization client accuracy test
self.cg_avg_data_train = [] # avg generalization client accuracy train
self.cs_avg_data_test = [] # avg specialization client test accuracy
self.cs_avg_data_train = [] # avg specialization client train accuracy
def __init__(self, params, learner, data):
for key, val in params.items():
setattr(self, key, val)
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(
*params['model_params'], self.inner_opt, self.seed)
self.clients = self.setup_clients(data, self.dataset, self.model,
self.client_model)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = self.client_model.get_params()
self.dim_model, self.dim_x, self.dim_y = self.setup_dim(
self.dataset, self.model)
# initialize system metrics
self.metrics = Metrics(self.clients, params)
def __init__(self, params, learner, dataset, dataset_adv=None):
# transfer parameters to self
for key, val in params.items(): setattr(self, key, val);
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(*params['model_params'], self.q, self.inner_opt, self.seed)
self.clients = self.setup_clients(dataset, self.data_partition_seed, dataset_adv, self.client_model)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = self.client_model.get_params()
# initialize system metrics
self.metrics = Metrics(self.clients, params)
def __init__(self, params, learner, dataset):
# transfer parameters to self
for key, val in params.items():
setattr(self, key, val)
self.parameters = params
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(*params['model_params'], self.inner_opt,
self.seed)
self.clients = self.setup_clients(dataset, self.client_model)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = self.client_model.get_params()
# initialize system metrics
self.metrics = Metrics(self.clients, params)
self.rs_train_acc, self.rs_train_loss, self.rs_glob_acc = [], [], []
def __init__(self, params, learner, dataset):
# transfer parameters to self
for key, val in params.items():
setattr(self, key, val)
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(*params['model_params'], self.inner_opt,
self.seed)
self.clients = self.setup_clients(dataset, self.client_model)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = self.client_model.get_params()
# initialize system metrics
self.metrics = Metrics(self.clients, params)
# Print the number of parameters
model_len = process_grad(self.latest_model).size
print('{} Parameters in Total'.format(model_len))
def __init__(self, params, learner, dataset):
# transfer parameters to self
for key, val in params.items():
setattr(self, key, val)
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(*params['model_params'], self.q,
self.inner_opt, self.seed)
self.clients = self.setup_clients(dataset, self.dynamic_lam,
self.client_model)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = copy.deepcopy(self.client_model.get_params(
)) # self.latest_model is the global model
self.local_models = []
self.interpolation = []
self.global_model = copy.deepcopy(self.latest_model)
for _ in self.clients:
self.local_models.append(copy.deepcopy(self.latest_model))
self.interpolation.append(copy.deepcopy(self.latest_model))
# initialize system metrics
self.metrics = Metrics(self.clients, params)
def __init__(self, params, learner, dataset):
# transfer parameters to self
for key, val in params.items():
setattr(self, key, val)
self.original_params = params
# create worker nodes
tf.reset_default_graph()
random.seed(123)
self.client_model_low = learner(*params['model_params'],
self.inner_opt,
self.seed,
device_type="low")
self.client_model_high = learner(*params['model_params'],
self.inner_opt,
self.seed,
device_type="high")
# Based on the drop percentage, some devices will get a smaller version of the model to train.
self.clients_low, self.clients_high = self.setup_clients(
dataset, self.client_model_low, self.client_model_high)
self.clients = self.clients_low + self.clients_high
print('{} Clients in Total'.format(params['batch_size']))
print('{} High End Clients, {} Low End Clients'.format(
self.original_params['batch_size'] -
int(self.original_params['drop_percent'] *
self.original_params['batch_size']),
int(self.original_params['drop_percent'] *
self.original_params['batch_size'])))
self.latest_model_low = self.client_model_low.get_params()
self.latest_model_high = self.client_model_high.get_params()
# initialize system metrics
self.metrics = Metrics(self.clients, params)
class BaseFedarated(object):
def __init__(self, params, learner, data):
for key, val in params.items():
setattr(self, key, val)
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(
*params['model_params'], self.inner_opt, self.seed)
self.clients = self.setup_clients(data, self.dataset, self.model,
self.client_model)
print('{} Clients in Total'.format(len(self.clients)))
self.latest_model = self.client_model.get_params()
self.dim_model, self.dim_x, self.dim_y = self.setup_dim(
self.dataset, self.model)
# initialize system metrics
self.metrics = Metrics(self.clients, params)
def __del__(self):
# self.client_model.close()
pass
##################################SET UP####################################
def setup_dim(self, dataset_name, model_name):
if model_name == 'mclr':
if dataset_name == 'adult':
return 104*2, 104, 2
elif dataset_name == 'mnist':
return 784*10, 784, 10
else:
raise "Unknown dataset and model"
def setup_clients(self, dataset, dataset_name, model_name, model=None):
'''instantiates clients based on given train and test data directories
Return:
list of Clients
'''
users, groups, train_data, test_data = dataset
if len(groups) == 0:
groups = [None for _ in users]
all_clients = [Client(id=u, group=g, dataset_name=dataset_name, model_name=model_name, # noqa: E501
train_data=train_data[u], eval_data=test_data[u], model=model) for u, g in zip(users, groups)] # noqa: E501
return all_clients
#################################TRAINING#################################
def train_grouping(self):
count_iter = 0
for i in range(self.num_rounds):
# loop through mini-batches of clients
for iter in range(0, len(self.clients), self.clients_per_round):
if count_iter % self.eval_every == 0:
self.evaluate(count_iter)
selected_clients = self.clients[iter: iter + self.clients_per_round]
csolns = []
########################## local updating ##############################
for client_id, c in enumerate(selected_clients):
# distribute global model
c.set_params(self.latest_model)
# local iteration on full local batch of client c
soln, stats = c.solve_inner(num_epochs=self.num_epochs, batch_size=self.batch_size)
# track computational cost
self.metrics.update(rnd=i, cid=c.id, stats=stats)
# local update
model_updates = [u - v for (u, v) in zip(soln[1], self.latest_model)]
# aggregate local update
csolns.append(model_updates)
######################### local process #########################
csolns_new=[]
for csoln in csolns:
flattened = process_grad(csoln)
tmp = []
processed_update = self.local_process(flattened)
tmp.append(np.reshape(processed_update[:self.dim_model], (self.dim_x, self.dim_y)))
tmp.append(processed_update[self.dim_model:])
csolns_new.append(tmp)
self.latest_model = [u + v for (u, v) in zip(self.latest_model, self.server_process(csolns_new))]
self.client_model.set_params(self.latest_model)
count_iter += 1
# final test model
self.evaluate(count_iter)
#################################EVALUATING###############################
def train_error_and_loss(self):
num_samples = []
tot_correct = []
losses = []
for c in self.clients:
ct, cl, ns = c.train_error_and_loss()
tot_correct.append(ct*1.0)
num_samples.append(ns)
losses.append(cl*1.0)
ids = [c.id for c in self.clients]
groups = [c.group for c in self.clients]
return ids, groups, num_samples, tot_correct, losses
def test(self):
'''tests self.latest_model on given clients
'''
num_samples = []
tot_correct = []
self.client_model.set_params(self.latest_model)
for c in self.clients:
ct, ns = c.test()
tot_correct.append(ct*1.0)
num_samples.append(ns)
ids = [c.id for c in self.clients]
groups = [c.group for c in self.clients]
return ids, groups, num_samples, tot_correct
def evaluate(self, i):
stats = self.test()
stats_train = self.train_error_and_loss()
train_loss = np.dot(stats_train[4], stats_train[2])*1.0/np.sum(stats_train[2])
train_acc = np.sum(stats_train[3])*1.0/np.sum(stats_train[2])
test_acc = np.sum(stats[3])*1.0/np.sum(stats[2])
tqdm.write('At round {} training loss: {}'.format(i, train_loss))
tqdm.write('At round {} training accuracy: {}'.format(i, train_acc))
tqdm.write('At round {} testing accuracy: {}'.format(i, test_acc))
self.metrics.accuracies.append(test_acc)
self.metrics.train_accuracies.append(train_acc)
self.metrics.train_losses.append(train_loss)
self.metrics.write()
#################################LOCAL PROCESS##################################
def local_process(self, flattened):
'''
DO NOTHING
1. non-private
2. no clipping
3. no sparsification
(for npsgd)
'''
return flattened
#################################AVERAGE/AGGREGATE##############################
def server_process(self, messages):
'''
ONLY AGGREGATE
weighted or evenly-weighted by num_samples
'''
if len(messages) == 1:
total_weight, base = self.aggregate_e(messages)
else:
total_weight, base = self.aggregate_w(messages)
return self.average(total_weight, base)
def average(self, total_weight, base):
'''
total_weight: # of aggregated updates
base: sum of aggregated updates
return the average update
'''
return [(v.astype(np.float16) / total_weight).astype(np.float16) for v in base]
def average_cali(self, total_weight, base, clip):
'''
total_weight: # of aggregated updates
base: sum of aggregated updates
return the average update after transforming back from [0, 1] to [-C, C]
'''
return [transform((v.astype(np.float16) / total_weight), 0, 1, -self.clip_C, self.clip_C).astype(np.float16) for v in base]
def aggregate_w(self, wsolns):
total_weight = 0.0
base = [0] * len(wsolns[0][1])
for w, soln in wsolns:
total_weight += w
for i, v in enumerate(soln):
base[i] = base[i] + w * v.astype(np.float16)
return total_weight, base
def aggregate_e(self, solns):
total_weight = 0.0
base = [0] * len(solns[0])
for soln in solns:
total_weight += 1.0
for i, v in enumerate(soln):
base[i] = base[i] + v.astype(np.float16)
return total_weight, base
def aggregate_p(self, solns):
_, base = self.aggregate_e(solns)
m_s = np.bincount(self.choice_list, minlength=(self.dim_model + self.dim_y))
m_n = np.ones(len(m_s))*self.m_p - m_s
assert len(np.where(m_n<0)[0]) == 0, 'ERROR: Please choose a larger m_p (smaller mp_rate) and re-run, cause {}>{}'.format(max(m_s), self.m_p)
dummies = np.zeros(len(m_n))
sigma = (2*self.clip_C/self.epsilon) * math.sqrt(2 * math.log(1.25/self.delta))
for i, v in enumerate(m_n):
assert self.mechanism == 'laplace', "Please use laplace for v1-v3"
dummies[i] = sum(np.random.laplace(loc=0.5, scale=1.0/self.epsilon, size=int(v))) - 0.5*(self.m_p-self.em_s)
d_noise = []
d_noise.append(np.reshape(dummies[:self.dim_model], (self.dim_x, self.dim_y)))
d_noise.append(dummies[self.dim_model:])
self.choice_list = [] # empty the choise list after each aggregation
return [transform( (v+noise)/self.em_s, 0, 1, -self.clip_C, self.clip_C).astype(np.float16) for v, noise in zip(base, d_noise)]
def __init__(self, params, learner, dataset):
# transfer parameters to self
for key, val in params.items():
setattr(self, key, val)
self.parameters = params
# create worker nodes
tf.reset_default_graph()
self.client_model = learner(*params['model_params'], self.inner_opt,
self.seed)
#initilzation of clients
self.Weight_dimension = 10
self.clients = self.setup_clients(dataset, self.client_model)
self.N_clients = len(self.clients)
self.TreeRoot = None
print('{} Clients in Total'.format(self.N_clients))
self.latest_model = self.client_model.get_params()
self.model_shape = (self.latest_model[0].shape,
self.latest_model[1].shape
) #weight, bias dimension
self.model_shape1 = []
for m in self.latest_model:
self.model_shape1.append(m.shape)
print("Model Shape1:", self.model_shape1)
print("Model Shape:", self.model_shape)
# initialize system metrics
self.metrics = Metrics(self.clients, params)
self.rs_train_acc, self.rs_train_loss, self.rs_glob_acc = [], [], []
# self.test_accs = np.zeros(K_Levels+1)
# self.train_accs = np.zeros(K_Levels+1)
self.count_grs = np.zeros(K_Levels + 1)
self.cg_avg_data_test = [] # avg generalization client accuracy test
self.cg_avg_data_train = [] # avg generalization client accuracy train
self.cs_avg_data_test = [] # avg specialization client test accuracy
self.cs_avg_data_train = [] # avg specialization client train accuracy
# self.gs_data_test = [] # specialization of group test accuracy
# self.gs_data_test.append(np.zeros(K_Levels+1 ))
# self.gg_data_test = [] # generalization of group test accuracy
# self.gg_data_train = [] # generalization of group train accuracy
# self.gs_data_train = [] # specialization of group train accuracy
# self.gs_data_train.append(np.zeros(K_Levels + 1))
self.cs_data_test = np.zeros((self.num_rounds, self.N_clients))
self.cs_data_train = np.zeros((self.num_rounds, self.N_clients))
self.cg_data_test = np.zeros((self.num_rounds, self.N_clients))
self.cg_data_train = np.zeros((self.num_rounds, self.N_clients))
self.gs_level_train = np.zeros(
(K_Levels + 1, self.num_rounds,
2)) # specialization of group train accuracy
self.gs_level_test = np.zeros(
(K_Levels + 1, self.num_rounds,
2)) # specialization of group test accuracy
self.gks_level_train = np.zeros(
(2, self.num_rounds)) # specialization of group k train accuracy
self.gks_level_test = np.zeros(
(2, self.num_rounds)) # specialization of group k test accuracy
self.gg_level_train = np.zeros(
(K_Levels + 1, self.num_rounds,
2)) # generalization of group train accuracy
self.gg_level_test = np.zeros(
(K_Levels + 1, self.num_rounds,
2)) # generalization of group test accuracy
self.gkg_level_train = np.zeros(
(2, self.num_rounds)) # generalization of group k train accuracy
self.gkg_level_test = np.zeros(
(2, self.num_rounds)) # generalization of group k test accuracy
self.dendo_data = []
self.dendo_data_round = []
