Data is the most important thing for any organization, any person, or anyone in the world. Everyone wants their data not to be breached, be it any person or any organization. But to train machine learning algorithms, it needs data, to be precise, a lot of quality data. Traditional machine learning methods save data in one server and then train the model. This method has possibilities of personal data breaches.
Federated learning (FL) is a machine learning method that enables machine learning models to train on different datasets located on different sites without data sharing. It allows the creation of a shared global model without putting training data in a central location. It also allows personal data to remain in local sites, reducing the possibility of personal data breaches. In federated learning, it is not data that moves to the model but the model that moves to the data. It means training happens with end devices.
Traditional machine learning uses all available training data in the central server to train the machine learning algorithms. It works fine, but it has several drawbacks:
Users demand fast response, and communication between the device and central server could be slow.
Continual learning becomes challenging if use edge computing.
Personal data may get breached.
How does Federated Learning works?
To train a machine learning model using Federated learning, data does not need to be on a central server. Federated learning uses decentralized techniques to train centralized models. The training in federated learning is iterative, meaning training can happen multiple times, enabling continuous learning and continuous knowledge sharing.
The first step is to choose a model either pre-trained or not trained at all, then, the distribution of the initial model to local devices or local servers.
Local machine learning models are trained on local datasets.
The results of the local models are shared with the cloud.
A shared global model is built.
Global models use aggregate values to find the best performance.
And then, characteristics of global models are shared with local data centers to integrate global models into local models.
Using a global share model allows many devices to learn collaboratively. It uses the data on your devices to update the model and will only send the collected information of the model (like parameters and results) to the cloud but not the data. It means it protects individual data by keeping it local. For example, using the sharing model, the keyboard model will predict the next word for you by keeping your text message safe. It is a decentralized machine learning technique and reduces the number of hardware infrastructure by keeping your data locally.
Federated learning is an emerging field in machine learning, and it already has more benefits than traditional machine learning approaches.
Data Security: Keeps training data locally on the devices, so a data pool is not required.
Data diversity: Heterogeneous data because it uses data from different users.
Real-time continual learning: Models are continuously improved with client data.
Hardware Efficiency: Use less complex hardware because federated learning does not need one complex central server to analyze.
What are the core challenges of Federated Learning?
The core challenges of Federated Learning are listed below:
Communication efficiency throughout the federated network.
Managing multiple systems in the same network.
Data in federated networks has statistical heterogeneity.
Concerns about privacy and methods to protect it.
Top 8 Applications of Federated Learning
Federated learning is essential in supporting privacy-sensitive data applications where training data is distributed. Theoretically, it sounds like a perfect plan that helps solve problems we face in traditional machine learning models with data on one central server or location. Federated learning is still being researched, but we already have a few applications.
By learning user behavior over a broad pool of mobile phones, statistical models are utilized to power apps like next-word prediction, facial recognition, and voice recognition. Users may refuse to share their data to protect their privacy or reduce their phone's bandwidth or battery use. Federated learning can create accurate smartphone predictions without exposing personal data or compromising the user experience.
Entire organizations or institutions might be considered "devices" in federated learning. Hospitals, for example, host a massive amount of patient data that may be used in predictive healthcare applications. Hospitals, on the other end, follow strong privacy policies and may be bound by legal, administrative, or ethical limitations that require data to be kept local. Federated learning is a good solution for these applications since it reduces network load and allows for private learning amongst different devices/organizations.
In modern IoT networks, such as wearable gadgets, autonomous cars, and smart homes, sensors are used to gather and react to data in real-time. To operate safely, a fleet of autonomous cars, for example, may require an up-to-date model of traffic, construction, or pedestrian behavior. However, building aggregate models in these cases may be difficult due to privacy issues and the restricted connection of each device. Federated learning approaches enable train models that respond to these systems' changes efficiently while maintaining user privacy.
Because protected health information cannot be shared easily due to HIPAA and other regulations, healthcare is one of the industries that can benefit the most from federated learning. In this approach, a large amount of data from various healthcare databases and devices may be used to construct AI models while remaining compliant with rules.
Personalization, as you know, is highly dependent on the data of each unique user. However, as more individuals become worried about how much data they would prefer not to disclose with others, sites like social networking, eCommerce platforms, and other locations come to mind. Advertising may use federated learning to remain afloat and ease people's anxieties while depending on personal data from consumers.
Because federated learning can provide real-time predictions, it's employed in developing self-driving automobiles. Real-time updates on road and traffic conditions may be included in the data, allowing for continuous learning and speedier decision-making. This may result in a more enjoyable and secure self-driving automobile experience. The automobile sector is a promising area for federated machine learning implementation. However, at present, all that is being done in this regard is research. One of the research studies showed that federated learning may shorten training time in self-driving car wheel steering angle prediction.
Federated learning in the field of financial fraud
Many multinational financial crimes have emerged as a result of the digital era. Financial crimes, fraudulent loans, and money laundering are all common sub-categories of financial crimes. Banks and customers suffer significant losses due to credit card fraud.
Integrating financial, medical, and other data from many sources is required when developing a data service platform for the insurance sector. Multi-party data must be considered if an insurance firm wishes to boost its risk management skills and business growth. Effective data utilization without intruding on personal privacy is also a significant concern in the insurance sector.
Personal data is very crucial for everyone. No one wants to share personal data with anyone. And also, to train a machine learning model, data being on centralized servers is one of the problems. Federated learning solves these problems and provides users or organizations with better performance. It trained models on local data in devices. Only the model's outputs are shared, not the data. So, the data is safe and not exposed to the central server. And you get better predictions and better performance.