Federated Machine Learning
Federated machine learning is a machine learning technique that allows businesses to leverage data from different sources without having to centralize the data. This type of learning essentially distributes the ability to train machine learning models across multiple devices or servers that contain data, allowing businesses to analyze sensitive data without it leaving the source device. This is particularly useful in industries such as healthcare or finance where data privacy is a top concern.
Traditional vs. federated machine learning
It is a common practice to build machine learning models by training them on a large dataset that is usually centralized. However, data sensitivity often poses challenges to data sharing, especially when developing a machine learning model requires large volumes of sensitive data. This can create an obstacle in achieving common goals.
How it works
This is achieved through a process of federation aggregation, where the local models are combined into a master model. The master model hyperparameters are maintained by the federated platform, which ensures that the final model is accurate and reliable. Only local models are shared, while individual data sets are kept secure and private.
Why federated machine learning matters
Federated machine learning enables organizations to build models while maintaining data privacy. This approach to machine learning has emerged not only as a solution to working with sensitive data but also allows to share data across organizations. With federated machine learning, each organization trains its own model on its own data, which is kept completely private.
What BranchKey does
With the BranchKey Platform, organizations have the tools they need to manage decentralized entities, monitor the health and performance of deployments in one single UI, and store large data files and machine learning models with the highest level of security. Learn more about our product.
