A Novel Technology for Cooperative Analysis of Big Data
Bonn (Germany), May, 26th, 2021. Communities benefit from sharing knowledge and experience among their members. Following a similar principle - called “swarm learning” - an international research team has trained artificial intelligence algorithms to detect blood cancer, lung diseases and COVID-19 in data stored in a decentralized fashion. This approach has advantage over conventional methods since it inherently provides privacy preservation technologies, which facilitates cross-site analysis of scientific data. Swarm learning could thus significantly promote and accelerate collaboration and information exchange in research, especially in the field of medicine. Experts from the DZNE, the University of Bonn, the information technology company Hewlett Packard Enterprise (HPE) and other research institutions report on this in the scientific journal “Nature”.
Science and medicine are becoming increasingly digital. Analyzing the resulting volumes of information - known as “big data” - is considered a key to better treatment options. “Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments,” said Joachim Schultze, Director of Systems Medicine at the DZNE and professor at the Life & Medical Sciences Institute (LIMES) at the University of Bonn. “It’s critical for science to be able to use such data as comprehensively and from as many sources as possible.”
However, the exchange of medical research data across different locations or even between countries is subject to data protection and data sovereignty regulations. In practice, these requirements can usually only be implemented with significant effort. In addition, there are technical barriers: For example, when huge amounts of data have to be transferred digitally, data lines can quickly reach their performance limits. In view of these conditions, many medical studies are locally confined and cannot utilize data that is available elsewhere.
Data Remains on Site
In light of this, a research collaboration led by Joachim Schultze tested a novel approach for evaluating research data stored in a decentralized fashion. The basis for this was the still young “Swarm Learning” technology developed by HPE. In addition to the IT company, numerous research institutions from Greece, the Netherlands and Germany - including members of the “German COVID-19 OMICS Initiative” (DeCOI) - participated in this study.
Swarm Learning combines a special kind of information exchange across different nodes of a network with methods from the toolbox of “machine learning”, a branch of artificial intelligence (AI). The linchpin of machine learning are algorithms that are trained on data to detect patterns in it - and that consequently acquire the ability to recognize the learned patterns in other data as well. “Swarm Learning opens up new opportunities for collaboration in medical research, as well as in business. The key is that all participants can learn from each other without having to share confidential data,” said Dr. Eng Lim Goh, Senior Vice President and Chief Technology Officer for artificial intelligence at HPE.
In fact, with Swarm Learning, all research data remains on site. Only algorithms and parameters are shared – in a sense, lessons learned. “Swarm Learning fulfills the requirements of data protection in a natural way,” Joachim Schultze emphasized.