It is universally acknowledged that the global artificial intelligence market is expanding at breakneck speed. With an average annual growth rate of 38.1 percent between 2022 and 2030 and a market value of over $136 billion, few could argue otherwise.
Tech juggernauts like Google and Apple are gathering data through machine learning to create exciting AI models, and advances are constantly being made in the AI/ML space. One such recent development in the field of AI and ML is federated learning. In simple terms, federated learning is a decentralized machine learning method that allows you to train AI models without letting anyone see or access your data. It enables you to unlock data for use in cutting-edge AI applications and guards against the compromise of sensitive information like personally identifiable information (PII).
In this article, you’ll learn more about federated learning, you’ll see how it works, its basic types, and finally, what benefits it offers.
Federated Learning in a Nutshell
Federated learning is essentially the process of distributing models across client devices, typically smart or edge devices, and sending updates from these models back to a central server to train a global model. This cross-device learning occurs without the exchange of any personal data. On-device training enables personalization that would not be possible otherwise while maintaining privacy through the use of a secure aggregation protocol.
How Federated Learning Works
Before we get into federated machine learning and how it works, let's see a few things about regular machine learning. Data is the starting point for machine learning! You need a lot of data, and that data can be collected from tens of thousands of devices or any other source that generates data for machine learning. All of this data is centralized within the corporate server or enterprise machines, and it is here that machine learning applications can be tested.
When an organization decides to apply machine learning to centralized training data, various algorithms, libraries, or machine learning frameworks are used to accomplish this. Your output is the machine learning model you generate over several iterations. Finally, the machine learning model is ready to be applied to the business process; it is adapted to existing data and produces the desired results. That's what we've seen in traditional machine learning, where training is focused on a single set of training data.
In order to better understand federated machine learning, let's look at an example in which a very large organization like Google applies it to mobile devices distributed worldwide. In this example, there are millions or even billions of devices in different people's hands.
All of these devices receive the machine learning model from the corporate server or corporate network, which is pushed by the enterprise, and the very first version of the machine learning model is quickly deployed to all of these machines. So, one thing should be clear: we are not moving the data in this process; rather, we are moving the model. Now, the machine learning model that is running on these devices begins learning from the data that is available in the machine and gradually improves itself. The improvements might be very specific to the activities that are occurring in the device and the data that is generated based on those activities.
Similarly, all of these N-number of devices begin improving and learning based on the activities they perform. Soon, each device will be able to improve its model, and that model update will be ready to be sent from the device to the parent servers. Now, every device accepts the model update that is being transmitted as the delta update to the prior model, and that delta update is sent to the parent server or parent network. This delta model update is then very quickly aggregated, different machine learning algorithms are applied to it, and finally, the model is updated by aggregating and averaging all the information updates that came from all of these devices.
And in this way, the new model is distributed to every single device that is prepared to carry out the subsequent iteration of federated learning. This process is repeated as often as required by the enterprise network, and this is how very large companies like Google and Facebook distribute federated learning or collaborative machine learning to every single device all over the world.
What Are The Benefits of Federated Learning?
In the machine learning field, federated learning is a new approach that trains models on decentralized data and has already proven to be considerably more advantageous than centralized, traditional methods.
Data security: Secure aggregation is used in federated learning to protect client updates from unauthorized access. Data is less vulnerable to breaches because it is easier to aggregate data on a central, external server. As a result, the server is unable to determine the value or origin of each user-provided model update and data attribution and inference attacks are less likely. Because personal data stays local, businesses that are subject to stringent privacy laws can benefit from the security that it offers.
Data diversity: The centralized model of federated learning allows for greater data diversity because it learns from multiple organizations and populations instead of from one dataset with a potentially unrepresentative demographic. As a result, the model becomes more representative and inclusive.
Fewer hardware requirements: Less complicated hardware is needed for federated learning models because they don't need a single, sophisticated central server to analyze data.
Enables collaboration: Mobile phones and other devices can learn a common prediction model collectively thanks to federated learning. This method prevents the need for the training data to be uploaded to and stored on a central server by keeping it locally on the device.
Reduces time: Predictions made using federated learning take place immediately on the device. This reduces the delay that occurs when raw data is sent back to a central server before the results are sent back to the device. The prediction process still operates without internet connectivity because the models are downloaded directly to the device.
Causes no disruption: Federated learning does not deplete the battery life of the devices used in training. In fact, devices participate in training only when users are not using them. Training can take place while your phone is charging, idle, or in do not disturb mode.
Types of Federated Learning
Overall there are three types of federated machine learning.
- Horizontal federated learning
- Vertical federated learning
- Federated transfer learning
Horizontal learning occurs when multiple nodes share the same fields. All devices are based on datasets with the same feature space, which means that Client A and Client B has the same set of features. For example, bank branches that use the same software may store the same information, like age, occupation, income, etc., about completely different users. If this data is kept locally at each branch, it is a good candidate for horizontal federated learning. Other examples include a network of hospitals, shipping companies, or manufacturers with similar assembly lines.
Vertical learning occurs when different fields with multiple nodes exist, regardless of whether the entries in the data have any relationship or not. To train a global model, different datasets with different feature spaces are used. For example, a bank and an energy utility share the same group of users, but the banks have information about the users' income or credit score, while the energy utility has information about their services, purchase history, usage data, etc.
Federated transfer learning works similarly to regular transfer learning in that generalized models with aggregators can be specialized for node-specific use. It can be used, for example, in fraud detection across banks operating in different geographies and accident prevention due to mechanical failure across an entire industry that uses similar equipment.
Applications of Federated Learning
Federated learning is crucial for supporting distributed training data applications that deal with privacy-sensitive data. Although federated learning is still under research, there are already a few applications that address issues that conventional machine learning models face. The following are only a few examples of how and where federated learning is used or can be used.
Healthcare Applications
Healthcare is one of the industries that can benefit the most from federated learning because health information cannot be shared easily due to various regulations that protect sensitive data. This method allows for the construction of AI models while adhering to regulations and making use of a variety of healthcare databases and devices.
Mobile Application Features
Apps like facial and voice recognition are powered by statistical models that analyze user behavior across a wide range of mobile phones. Users might choose not to share their information out of concern for their privacy, but federated learning can produce precise smartphone predictions without disclosing personal information or degrading the user experience.
Internet of Things (IoT)
Sensors are used in contemporary IoT networks, such as wearable technology, autonomous vehicles, and smart homes, to collect and process data in real time. A fleet of autonomous vehicles, for instance, might need a current simulation of pedestrian, construction, or traffic behavior to function safely. However, due to privacy concerns and the constrained connectivity of each device, creating aggregate models in these situations may be challenging. Federated learning techniques enable the development of models that quickly adapt to these systems' changes while protecting user privacy.
Conclusion
Federated learning is becoming more and more popular because it solves some core issues of modern artificial intelligence. Federated learning is constantly being implemented in new artificial intelligence applications, and researchers are continually looking for new approaches to overcome its limitations.
Numerous new applications using federated learning will be developed and will improve user experience in ways that were not previously possible. Many businesses will step forward and offer a platform for quickly creating federated learning applications. It will be fascinating to watch how the field develops in the future.
If you're more of a visual learner and want to understand federated learning, Google has created a fun and easy-to-follow guide for you.
Still, if you have any questions about federated learning, we at Solwey can help you out. We understand technology and can leverage the most suitable tools to help your business grow. Reach out and find out how Solwey and our custom-tailored software solutions can cover your needs.
