by Subhaditya Mukherjee
Today we will talk about Federated Learning
Want the smarts machine learning offers? That is awesome, but how secure is your data there really? And what if you could have the benefits without there being a chance of your data being leaked?
We will have a look at Federated Learning and understand how it works. We will also look at a few papers and what they have to say on the matter and take the library Syft for a whirl.
Think about this from the perspective of an app that connects you with your doctor. It is a great app, and even has the ability to forecast future appointments and sometimes even suggest common medication. Now one fine day the company gets sold and all your data is in someone else’s hands. And not just medical information, but what the Deep Learning algorithm learned about you and your behaviour. Just data privacy is great for standard data like medical reports etc. But what about a DL algorithm? What about a model that learns how you speak, how you text your friends, what food you like?
Scary isn’t it? Now let us look at how we can prevent Company X from having more information on you than you have about yourself.
Drumroll.. Let us first understand what we want to do. Consider a keyboard app. Say we have 200 users of our keyboard app.(Not very popular sadly). Now this is an intelligent keyboard and as time goes by, it learns how the users type, what words they use and is even able to predict what they might say to a given sentence. We want to make sure that if there is a leak, it should not be possible for whoever gets it to identify a unique model for the user.
You might think, hey that’s easy. Let’s just train the network on their phones! You are right. Almost. The only issue is that our phones cannot afford to train the entire model. And if we use a pretrained one? Well.. the predictions are just not personalized enough.
So how about we train the model first on huge clusters of computers and then send the pretrained ones to the phones? Perfect. How about personalization? Why not update the model on the phone? That’s great. But how will the main model get better? If we send the model this phone has learnt, it would defeat our purpose. How about we aggregate the learning across users?
There we go.. lets call that federated learning :)
P.S (The keyboard example is from the Google keyboard paper)
Let us dig into the steps involved in making a Deep learning algorithm and identify what changes we need to make
Yes! There are many ways but the easiest by far is by using a library in Pytorch called Syft. It is as easy as adding a few lines to your code.
(Since the entire code is huge, I will only put snippets here. For the entire code, refer to my repository)
import syft as sy hook = sy.TorchHook(torch) bob = sy.VirtualWorker(hook, id="bob") # person1 alice = sy.VirtualWorker(hook, id="alice") # person2
train_loader = sy.FederatedDataLoader( # this is now a FederatedDataLoader datasets.MNIST('/home/eragon/Documents/datasets', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])) .federate((bob, alice)), # This will simulate a federated learning system shuffle=True, **kwargs )
def train(args, model, device, train_loader, optimizer, epoch): model.train() # Setting model to train device = torch.device("cuda") # Sending to GPU for batch_idx, (data, target) in tqdm(enumerate(train_loader)): model.send(data.location) # Send to location (worker) data, target = data.to(device), target.to(device) optimizer.zero_grad() # Reset grads output = model(data) # Passing batch through model loss = F.nll_loss(output, target ) loss.backward() # Backprop optimizer.step() # Pass through optimizer model.get() # Get it back if batch_idx % args.log_interval == 0: loss = loss.get() if args.dry_run: break
It could be used in a lot of places but for now it is still in testing stage. Although there have been a few early adopters. Some of the ones I found are:
This is an upcoming field and there are many things left to be done. Contribute to the libraries if you can, or just learn how things work and why they do. Want to read further? Look at the resources section. You will find many interesting links.
Thank you! Do reach out if you have any queries.