What is Federated Learning in Machine Learning?

Our world has changed completely from the day Artificial Intelligence was introduced. Well, Machine learning is also an important part of this and the way we are training the machine learning models has come a long way. In the traditional approach, all of the data was gathered at a central server and this created privacy issues for the sensitive information. To face this issue, AI models started to shift to a concept called "federated learning".

Here in this article, we will discuss in detail about Federated Learning. So if you are looking to grow your career in this field, you can enroll in the Machine Learning Online Training. This training will help you understand the fundamentals of machine learning. So let’s begin by understanding what is federated learning.

What is Federated Learning?

Federated learning is a way of training machine learning models without having to share data from individual devices to a central server. Instead, the data stays on each device, and the model is trained locally on that device. After training, the improvements made on each device are shared and combined to create a better overall model. This approach helps keep data private since it never leaves the device, and only the model updates are shared. 

How Does Federated Learning Work?

In federated learning, a basic model is stored on a central server. Copies of this model are sent to client devices, such as smartphones or IoT devices. These devices then train their versions of the model using the data they collect locally.

As time goes on, the models on these devices become more personalized, leading to a better experience for the user.

Next, the updates from each device—called model parameters—are sent back to the central server in a secure way. The server combines and averages the updates from all devices to create a new version of the model. Because the data comes from many different sources, the model becomes more flexible and able to handle a variety of situations.

Types of Federated Learning

We have mentioned the types of Federated Learning here in detail. As we know machine learning is a part of AI. So if you have completed the Generative AI Course then this may benefit you in the future.

Centralized Federated Learning

Centralized federated learning includes a central server that is key in coordinating the training process. At the start, the server selects which client devices will participate, and during the training, it collects updates from these devices. All communication occurs between the central server and the individual devices.

While this approach can create accurate models and is relatively simple, it comes with a drawback: the central server can become a bottleneck. The whole training process could be disturbed if there’s a network failure or issue with the server.

Decentralized Federated Learning

Decentralized federated learning does not rely on a central server. Instead, the model updates are shared directly between the interconnected edge devices. Each device aggregates the local updates from other devices to form the final model.

This approach eliminates the risk of a single-point failure since there’s no central server to be disrupted. However, the accuracy of the model depends on how well the edge devices are connected and the overall network setup.

Heterogeneous Federated Learning

Heterogeneous federated learning (HeteroFL) involves using different types of client devices, such as mobile phones, computers, or IoT devices. These devices can vary in hardware, software, computing power, and data types.

HeteroFL was created to address the limitations of traditional federated learning, which assumes that the local models are similar to the main model. In reality, this is rarely the case. HeteroFL can handle these differences and generate a single global model that works well for all types of devices, even with varying local models.

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Conclusion

From the above discussion, it can be said that Federated learning (FL) is a decentralized method for training machine learning models that offers benefits like better privacy protection, data security, and the ability to use different types of data. Unlike traditional centralized machine learning, FL allows us to build more accurate and adaptable models without the need for the data to leave the client devices. This helps keep the data secure and private.