The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often require data to be stored on a single machine with compute performed by a single node, making them unsuitable for decentralised training on multiple devices. This deficiency has motivated the development of federated learning algorithms, which allow multiple data owners to train collaboratively and use a shared model whilst keeping local data private. However, many of these algorithms focus on obtaining point estimates of model parameters, rather than probabilistic estimates capable of capturing model uncertainty, which is essential in many applications. Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. In this paper we introduce partitioned variational inference (PVI), a general framework for performing VI in the federated setting. We develop new supporting theory for PVI, demonstrating a number of properties that make it an attractive choice for practitioners; use PVI to unify a wealth of fragmented, yet related literature; and provide empirical results that showcase the effectiveness of PVI in a variety of federated settings.

For example, San Jose-based RPA firm Automation Anywhere recently worked with a pharmaceutical company in Europe to accelerate the research and approval of COVID-19 vaccines by augmenting reporting. RPA startup UiPath has also assisted with efforts around the pandemic, for instance helping the U.S. Department of Homeland Security use software bots to perform coronavirus-related data analysis. Deloitte reports that organizations that have implemented and scaled RPA see a return on investment within 12 months. And according to Everest Group, top performers earned nearly four times on their RPA investments while other enterprises earned nearly double. This isn't to suggest that RPA is without its challenges.

Symantec vice president global portfolio and solutions product management for enterprise security Bhagwat Swaroop (pictured) has been involved in the process of setting a course for the company following the divestiture of Veritas. He told iTWire that Symantec's unified security strategy has four legs: Machine learning is "finding its way into everything we do," he said. Symantec started applying machine learning more than a decade ago, and now has a team of between 30 and 40 people working to apply it to autonomous anomaly detection. Rather than focusing on detecting specific attributes of malware, spam and so on (which the Bad Guys can learn to work around), the idea is to take a more dynamic approach by examining relationships instead. This give "top of the line" detection of new threats, Swaroop said, with the lowest rate of false positives.