client.py

import models, torch, copy
class Client(object):

	def __init__(self, conf, model, train_dataset, id = -1):
		
		self.conf = conf
		
		self.local_model = models.get_model(self.conf["model_name"]) 
		
		self.client_id = id
		
		self.train_dataset = train_dataset
		
		all_range = list(range(len(self.train_dataset)))
		data_len = int(len(self.train_dataset) / self.conf['no_models'])
		train_indices = all_range[id * data_len: (id + 1) * data_len]
		
		self.mask = {}
		for name, param in self.local_model.state_dict().items():
			p=torch.ones_like(param)*self.conf["prop"]
			if torch.is_floating_point(param):
				self.mask[name] = torch.bernoulli(p)
			else:
				self.mask[name] = torch.bernoulli(p).long()
			
		self.train_loader = torch.utils.data.DataLoader(self.train_dataset, batch_size=conf["batch_size"], 
									sampler=torch.utils.data.sampler.SubsetRandomSampler(train_indices))
									
		
	def local_train(self, model):

		for name, param in model.state_dict().items():
			self.local_model.state_dict()[name].copy_(param.clone())

		optimizer = torch.optim.SGD(self.local_model.parameters(), lr=self.conf['lr'],
									momentum=self.conf['momentum'])
			
		self.local_model.train()
		for e in range(self.conf["local_epochs"]):
			
			for batch_id, batch in enumerate(self.train_loader):
				data, target = batch

				if torch.cuda.is_available():
					data = data.cuda()
					target = target.cuda()
			
				optimizer.zero_grad()
				output = self.local_model(data)
				loss = torch.nn.functional.cross_entropy(output, target)
				loss.backward()
			
				optimizer.step()
						
			print("Epoch %d done." % e)	
			
		diff = dict()
		for name, data in self.local_model.state_dict().items():
			diff[name] = (data - model.state_dict()[name])
			#print(diff[name].size(), self.mask[name].size())
			diff[name] = diff[name]*self.mask[name]

		return diff