With its potential to exceed the limits of centralised machine learning, federated learning is one of the most revolutionary developments in the field of data science and Artificial Intelligence. From Google and Uber to Facebook and Microsoft, it has been used by a variety of tech giants and is here to expand the possibilities for data scientists.
Wondering how federated learning works, in which industries it can be used, or what its advantages are? Through this article, we will take a deep look into this disruptive technology and answer all the questions in your mind.
What Is Federated Learning?
Federated learning (FL), or collaborative learning, is a machine learning method that allows a group of decentralised edge devices or systems to train machine learning models without needing to move or store the raw data in a central server.
Training in federated learning applications uses an iterative process, which means it can happen multiple times on a variety of devices or servers. In summary, FL learning works as follows:
- Centralised training: A central server creates a base model and sends it to Edge MLops devices.
- Local Training: Each device receives pre-trained or non-trained ML models and trains them with their local data. The devices do not share their raw data with each other or with the central server.
- Model aggregation: The devices send their locally trained models back to the central server. The server then aggregates the local models to build a shared global model.
- Model evaluation: The model updates are evaluated to determine the accuracy and identify any improvements over the previous version.
- Repeat: The shared model process is repeated until the model reaches a satisfactory level of accuracy or until a predetermined number of iterations has been reached.
Although the concept sounds hard to reach or adapt to, federated learning methods are already deployed by a variety of companies in different industries.
For example, during the last few years, scientists from Google have used this approach to improve next-word prediction models in their Gboard on Android. On the other hand, another tech giant, Apple, used it to improve the performance of the Siri voice assistant, personalise the maps feature on iPhones, and enhance the user experience by offering improved privacy.
There are two main reasons why these key players utilised this deployment model. First, it keeps personal data safe and secure with its on-device learning methods.
Second, a federated learning system can be used to solve a wide range of problems in a variety of industries, including logistics, energy, defence, telecommunications, insurance, connected vehicles, and industry 4.0.
Although federated learning practices are generally used to predict critical events, diagnose malfunction, optimise control, or improve the outputs in operational processes, they can be customised according to the needs and goals of businesses.
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Federated Learning vs. Classical Machine Learning
In classical machine learning, data is typically collected and stored centrally, and the model is trained on this central dataset, which requires sensitive data to be exchanged between a central cloud location and devices. In contrast, as we stated before, the federated learning platform doesn’t move data to the models, it moves models to the data.
Another key difference between federated and classical machine learning is that FL typically involves training models in a distributed manner, with each server or device contributing to the training process.
On the other hand, classical machine learning methods train models on a single machine owned and operated by a particular resource.
Advantages of Federated Learning
Considering the key differences between the two approaches, we can say that when compared with federated learning, classical machine learning models fall short to deploy AI systems and IoT practices in a cost-effective, secure way.
Now that we understand the basics and differences of FL, we can take a closer look at the benefits and values of deploying this model.
Enhanced Data Privacy and Security
Thanks to its collaborative nature, FL treats each IoT device at the edge as an individual client and trains models without transmitting the raw data. This means during the FL process, each IoT device only gathers the information needed to complete its task.
As FL keeps raw data on the device and only sends model updates to the central server, it protects private raw data, minimises the risk of personal data leakage, and guarantees the security of operations.
Increased Scalability and Cost-Efficiency
In data science, more data means better models. Still, when all the dataset is stored in one server or cloud, training a model with extended datasets can require a long time.
As federated learning allows keeping datasets in multiple locations and training models on each piece of data simultaneously, it accelerates the AI and IoT deployment models and improves the scalability of the operations.
Additionally, as FL trains the model at the edge and moves only the trained weights, by orders of magnitude, it reduces the volume of data that has to be transmitted to a network and processed in the cloud.
This way, it eliminates the cost of abundant data transfer and decreases computing and storage costs.
Generally, federated learning models can be applied to new situations without the need for retraining as models can learn from previous experiences and make predictions based on them. Additionally, the improvement and knowledge that this learning method gets from a particular field can be applied to another one.
For example, if the model learns to predict outcomes more efficiently in a field, it can apply this knowledge to another one to improve efficiency, reduce costs, and accelerate the process.
Improved Data Accuracy and Diversity
When data is centralised and used to train a model, it may not accurately represent the full range of data that the model that will be applied to.
On the other hand, training the models on decentralised data from a variety of sources and being exposed to a wider range of data can improve the model’s ability to generalise new data, handle variations, and reduce bias.
Exceeding Beyond Federated Learning…
In today’s world, federated learning is the key privacy-enhancing technology for data scientists and companies to access a wider range of data, train higher-quality AI models, and increase the ROI of AI and data science projects.
With our end-to-end distributed Edge AI platform, OctaiPipe, we are going one step further and combining the powers of Edge MLops, Federated learning, and out-of-the-box AutoML solutions. Together with OctaiPipe, you can get the best out of accelerated AI, federated intelligence, private AI, reduced data costs, and automated MLOps!