"Collaborative Machine Learning That Preserves Privacy"

To effectively train a Machine Learning (ML) model to perform a task, such as image classification, thousands, millions, or even billions of example images must be shown to the model. Gathering such massive datasets can be especially difficult when privacy is a concern, as with medical images. Therefore, researchers from MIT and the MIT-born startup DynamoFL have made federated learning, a popular solution to this problem, faster and more accurate. Federated learning is a collaborative method for training an ML model that preserves the privacy of sensitive user data. Hundreds or thousands of users train their own models on their own devices using their own data. Users then send their models to a central server, which combines them to create a better model, which it then sends back to all users. A group of hospitals from around the world, for example, could use this method to train an ML model that detects brain tumors in medical images while keeping patient data safe on their local servers. However, there are some disadvantages to federated learning. Transferring a large ML model to and from a central server requires moving a large amount of data, which has high communication costs, especially since the model must be sent back and forth many times. Furthermore, because each user collects their own data, that data does not necessarily follow the same statistical patterns, hampering the combined model's performance. The researchers developed a method for addressing these federated learning issues. Their method improves the accuracy of the combined ML model while significantly reducing its size, allowing users and the central server to communicate more quickly. It also ensures that each user receives a model that is more tailored to their specific environment, thus improving performance. This article continues to discuss the researchers' technique that improves the accuracy and efficiency of federated learning. 

MIT News reports "Collaborative Machine Learning That Preserves Privacy"