Cooperative learning: Decentralized data neural network

Noah Lewis, Sergey M. Plis, Vince D. Calhoun

Abstract: Researchers often wish to study data stored in separate locations, such as when several research entities wish to make inferences from their combined data. The most common solution is to centralize the data in one location. However, certain types of data can be difficult to transfer between entities due to legal or practical reasons. This makes centralizing these types of data problematic. A possible solution is the use of methods that learn from data without moving them to a central location: decentralized algorithms. Only a few algorithms emphasizing that property are known to us, and even fewer are used in the biomedical domain. In this paper, we propose a decentralized neural network that allows data analysis without transferring the data from the sites that host them. Instead, this method only transfers the gradients (or their parts) calculated via back-propagation. Our approach allows us to learn a classifier even when class examples are located at different sites, enabling privacy-aware collaboration across groups with specific research interests. We validate the method in several experiments to test stability, compare performance to a network trained on the centralized data, and investigate the ability to reduce size of data transfer. Our experiments on simulated, benchmark, and neuroimaging addiction data provide strong evidence that the proposed model works as effectively as a pooled centralized model.