Types of Federated Learning
There are various strategies that are used for Federated Learning. Let’s take a brief look at them.
- Centralized Federated Learning: In this, a central server is used to perform different steps of the algorithm. The central system is subjected to selecting the nodes at the beginning of the training process and then it is also responsible for aggregating the model updates that we received from different nodes/devices. Here, all the selected nodes, send the updates to this central server and hence it is the bottleneck of the system. This method can cause bottleneck problems.
- Decentralized Federated Learning: In this, the nodes themselves can coordinate to get the updated model. This approach can help in preventing the single server problems, that we can get from the centralized federated learning, as in this the model updates are shared between the interconnected nodes without the need of the central system. Here, the model’s performance is totally dependent on what network topology we opt for.
- Heterogeneous Federated Learning: This learning involves a large no of heterogenous clients e.g., mobile devices, and IoT devices. These devices can differ in software or hardware configurations. Recently, a Federated learning framework called HeteroFL has emerged, specifically designed to tackle the challenges posed by heterogeneous clients with varying computation and communication capabilities.
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
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