What is Federated Learning?
Federated Learning is a technique of training machine learning models on decentralized data, where the data is distributed across multiple devices or nodes, such as smartphones, IoT devices, edge devices, etc. Instead of centralizing the data and training the model in a single location, in Federated Learning, the model is trained locally on each device and the updates are then aggregated and shared with a central server.
Federated Learning in simple terms
To state a technical definition, I would say federated learning is to help learn a shared prediction model while maintaining all the training data on the device (mobile phone here specifically). This concept is purely based on Machine Learning. To be more specific it caters to mobile devices. We know that to perform modeling through a machine learning algorithm we need a sufficient amount of data to get the right prediction accuracy and then to implement it into production. Federated learning is a concept that eliminates the issue of storage of large training data. Data is stored at multiple locations. These locations are nothing but our devices.
Collaborative Learning – Federated Learning
The field of data science has seen significant developments in the past five years. Traditional Machine Learning training relied on large datasets which were stored in centralized locations like data centers, and the goal was to get accurate predictions and generate insights that will profit us in the end. But, this approach came with challenges like data storage issues, privacy concerns, and processing.
Recently, there has been a key development of the concept of federated learning, which is providing some groundbreaking solutions. This concept was coined by Google AI through its blog post. The title of the blog post was “Federated Learning: Collaborative Machine Learning without Centralized Training Data”.
In this article, we will simplify this term and understand what exactly is Federated Learning in simple terms, and its types, and also will see a real-life application where this is actually present in the backend. We will also try to skim through some of the benefits of the same.
Table of Content
- What is Federated Learning?
Types of Federated Learning- How Federated Learning work?
- Real-Life Application of Federated Learning
- Advantages of Federated Learning
- Disadvantages of Federated Learning