Federated Learning and its advantages
Let’s take an example to understand how FL architecture works, say there is a network of IoT devices that send data to a centralized server that uses the data to train a model and make predictions. What if this data that is transmitted over the network is confidential and could be used to manipulate some important decisions, this is where FL architecture could be handy. As we are using IoT devices we can integrate them with little more intelligence to train the model by themselves, but there could be a problem if it’s just a standalone device it may not be exposed to wide distributions of data to train the model, so what we can do is that once the devices train a basic model the model parameters are sent to the server for aggregation and this aggregated model is sent back to all the devices for making better predictions, even if the model parameters are leaked there are very little chances of inferring something from that parameters. In this way, we are not compromising data privacy and we are reducing the cost of transmitting bulky data over the network.
The formal definition of FL – “Federated learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them.”. The basic idea of FL architecture that we saw in the example is not a foolproof method and the architecture itself has some lapses and vulnerabilities presenting us with some challenging security issues to deal with. Let’s see some threat models and poisoning attacks in brief and in simple terms, understanding these models and attacks could help us design a foolproof privacy-preserving FL protocol.
Threats and vulnerabilities in Federated Learning
Prerequisites – Collaborative Learning – Federated Learning, Google Cloud Platform – Understanding Federated Learning on Cloud
In this article, we will learn review what is federated learning and its advantages over conventional machine learning algorithms. In the later part let’s try to understand the threats and vulnerabilities in federated learning architecture in simple terms.