Collaborative machine learning that preserves privacy
Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially challenging when privacy is a concern, such as with medical images. Researchers from MIT and the MIT-born startup DynamoFL have now taken one popular solution to this problem, known as federated learning, and made it faster and more accurate.
Generative adversarial networks (GANs), a class of machine learning frameworks that can generate new texts, images, videos, and voice recordings, have been found to be highly valuable for tackling numerous real-world problems. For instance, GANs have been successfully used to generate image datasets to train other deep learning algorithms, to…
Researchers from the McKelvey School of Engineering at Washington University in St. Louis have developed a machine learning algorithm that can create a continuous 3D model of cells from a partial set of 2D images that were taken using the same standard microscopy tools found in many labs today.