Revolutionizing Data Collaboration with Federated Machine Learning

From healthcare and government to the financial sector and beyond, advanced data science models and big data projects are unlocking insights that can deliver everything from novel approaches to preventing and treating disease to highly effective financial fraud detection and more. Organizations looking to embark on data collaboration initiatives must overcome obstacles such as data ownership issues, compliance requirements for a variety of regulations and more. In today's data-filled world, ensuring privacy and security is paramount, and the measures to which organizations must go to achieve this can make collaborative data science difficult. The potential consequences of sustaining any kind of privacy or security breach (noncompliance, fines, reputational damage, etc.) can cause organizations to shy away from sharing data sets that could spark the next life-saving medical treatment or momentous public service program. Luckily, organizations across many industries are recognizing just how much upside we're leaving on the table if valuable data sets remain siloed.