What is Federated Learning: Artificial Intelligence Explained




Various interconnected devices like smartphones

Federated learning is a machine learning approach that enables models to learn from data located at different devices or servers, without the need to share the data itself. This innovative method is designed to address the privacy and security concerns associated with traditional centralized machine learning models, where all data is collected and processed in one place. Federated learning allows for the creation of robust models while ensuring data privacy.

As we delve into the world of federated learning, we will explore its key concepts, its benefits and challenges, its applications, and its future prospects. This comprehensive glossary article will serve as a guide to understanding federated learning in the context of artificial intelligence, providing detailed explanations of each aspect of this fascinating field.

Concept of Federated Learning

Federated learning is based on the principle of decentralized learning. Instead of bringing all data to a central server for training, the learning process is distributed across multiple devices or servers, each holding a portion of the overall data. The models are trained locally on each device, and only the model updates (not the data) are shared with a central server where they are aggregated to form a global model.

This approach ensures that sensitive data remains on the local device, thereby enhancing data privacy. It also reduces the amount of data that needs to be transferred between devices and the central server, which can lead to significant savings in terms of bandwidth and computational resources.

Working of Federated Learning

The working of federated learning involves several steps. Initially, a global model is trained on a central server using a subset of the available data. This model is then distributed to the participating devices. Each device uses its local data to train the model, resulting in a local model update. These updates are then sent back to the central server.

At the server, these updates are aggregated using an algorithm, often a form of weighted average, to create an updated global model. This model is then sent back to the devices for further training. This process is repeated until the model reaches a desired level of accuracy.

Types of Federated Learning

There are primarily two types of federated learning: horizontal federated learning and vertical federated learning. Horizontal federated learning, also known as sample-based federated learning, is used when different devices have data from the same feature space but different samples. For instance, two hospitals may have patient data for the same set of diseases (features), but different patients (samples).

On the other hand, vertical federated learning, or feature-based federated learning, is used when different devices have data from the same sample space but different features. For example, a hospital and a pharmacy may have data for the same set of patients (samples), but different types of data (features).

Benefits of Federated Learning

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One of the primary benefits of federated learning is enhanced data privacy. Since the data never leaves the local device, it is less susceptible to breaches. This is particularly beneficial in sectors like healthcare and finance, where data privacy is of utmost importance.

Federated learning also reduces the need for data centralization, which can be a complex and resource-intensive process. By allowing models to be trained on the devices where the data is generated, federated learning can lead to significant savings in terms of data storage and transfer costs.

Improved Model Performance

Another advantage of federated learning is the potential for improved model performance. Since the models are trained on a diverse range of data from multiple devices, they can capture a wider range of patterns and trends, leading to better generalization and prediction accuracy.

Moreover, federated learning allows for real-time model updates. As soon as new data is generated on a device, it can be used to update the model. This can be particularly beneficial in dynamic environments where data patterns change rapidly.


Federated learning is highly scalable. It can accommodate a large number of devices, each with its own set of data. This makes it a suitable choice for applications involving big data.

Furthermore, since the computational load is distributed across multiple devices, federated learning can handle larger models and datasets than would be feasible with a single device or server.

Challenges of Federated Learning

Despite its many benefits, federated learning also comes with its share of challenges. One of the primary challenges is the issue of data heterogeneity. Since the data is distributed across multiple devices, there can be significant variation in terms of data quality, quantity, and distribution. This can affect the performance of the global model.

Another challenge is the issue of communication overhead. While federated learning reduces the amount of data that needs to be transferred, it still requires the model updates to be communicated between the devices and the central server. This can be a significant overhead, particularly in scenarios involving a large number of devices.

Security Concerns

While federated learning enhances data privacy, it also introduces new security concerns. For instance, an attacker could potentially manipulate the model updates sent to the central server, leading to a compromised global model.

Moreover, even though the data remains on the local device, an attacker could potentially infer sensitive information from the model updates. This is known as a model inversion attack. Therefore, appropriate security measures need to be in place to mitigate these risks.


Federated learning is inherently more complex than traditional machine learning. It requires sophisticated algorithms for aggregating the model updates, dealing with data heterogeneity, and ensuring model convergence. This can make it more difficult to implement and maintain.

Furthermore, federated learning requires the devices to have sufficient computational resources for training the models. This may not be feasible for all devices, particularly those with limited processing power or battery life.

Applications of Federated Learning

Federated learning has a wide range of applications across various sectors. In healthcare, it can be used to develop predictive models using data from multiple hospitals, without compromising patient privacy. In finance, it can be used to detect fraudulent transactions based on data from multiple banks.

In the field of mobile computing, federated learning can be used to improve the performance of predictive text models, by learning from the typing patterns of millions of users without accessing their personal messages. Similarly, in the field of Internet of Things (IoT), federated learning can be used to train models on data from a multitude of devices, without the need for data centralization.

Future Prospects

With the increasing awareness about data privacy and the growing volume of data being generated at the edge, the demand for federated learning is expected to rise in the coming years. Advances in technology, such as 5G and edge computing, are also expected to facilitate the adoption of federated learning.

Furthermore, research is ongoing to address the challenges associated with federated learning, such as data heterogeneity and communication overhead. New algorithms are being developed for more efficient model aggregation and better handling of data heterogeneity. Techniques are also being explored to reduce the communication overhead, such as model compression and selective model update sharing.


Federated learning is a promising approach to machine learning that offers a solution to the privacy and security concerns associated with traditional centralized models. While it comes with its own set of challenges, the potential benefits in terms of data privacy, resource efficiency, and model performance make it a compelling choice for many applications.

As we continue to generate more and more data at the edge, and as our need for data privacy continues to grow, federated learning is likely to play an increasingly important role in the field of artificial intelligence. Through this comprehensive glossary article, we hope to have provided you with a thorough understanding of this fascinating field.

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