Security Update: Data That Never Leaves Your Device
Decentralized training that doesn’t compromise data privacy.
Federated learning is a new and significant approach to artificial intelligence that is changing the way machine learning models are trained. This approach is strikingly different to traditional centralized machine learning techniques where all the local datasets are uploaded to one server. The main advantage of federated learning is that it allows AI models to be trained on data that is distributed across different devices and locations, while maintaining the privacy and security of the data.
Federated learning is a distributed approach to machine learning that enables the training of AI models without having to transfer data to a central server. Instead, the training takes place on the devices themselves, and the results are sent back to the central server. This approach has a number of advantages over traditional centralized machine learning, including:
Improved Privacy: One of the biggest advantages of federated learning is the improved privacy it provides. Because the data remains on the devices, there is no need to share the data with a central server. This means that sensitive data, such as personal health information, can be kept private and secure.
Reduced Data Transfer: Another advantage of federated learning is that it reduces the amount of data that needs to be transferred. With traditional centralized machine learning, all the data needs to be transferred to a central server, which can be time-consuming and expensive. Federated learning, on the other hand, only transfers the results of the training, which is a much smaller amount of data.
Improved Security: Federated learning also provides improved security over traditional centralized machine learning. Because the data remains on the devices, there is no central point of failure that can be targeted by hackers. This makes federated learning much more secure and less vulnerable to cyberattacks.
Increased Efficiency: Federated learning can also improve the efficiency of the machine learning process. Because the training takes place on the devices themselves, the process can be completed much faster than with traditional centralized machine learning. This can lead to faster development times and more efficient use of resources.
An example of federated learning has been seen in medicine. A recent study examined the role of data aggregated from multiple clinical sites in the management of AI-assisted CT analysis. This collaboration allowed a broader set of patients for data inclusion and expanded the role of collaboration in important clinical areas—all while prioritizing the privacy of patient data.
Federated learning allows individual hospitals to benefit from the rich datasets of multiple non-affiliated hospitals without centralizing the data in one place. This practice overcomes critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. Hence, federated learning allows multiple collaborators to build a robust machine-learning model using a large dataset.
Federated learning is an exciting new approach to artificial intelligence that has a number of advantages over traditional centralized machine learning. By keeping the data where it is, federated learning provides improved privacy, reduced data transfer, improved security, and increased efficiency. With the growing importance of cybersecurity and privacy, federated learning is a technology that is sure to become increasingly important in the not too distant future.
