Federated Analytics is the latest feature added to Raven Distribution Framework that allows for the safe dynamic aggregation of statistics such as mean, variance, and standard deviation across data that is privately held on several clients. RDF's Ravop library now supports the creation of federated operations which developers can leverage to conduct analysis without directly observing a client's private data. Federated analytics is a new approach to data analysis in which key statistics like mean, variance, and standard deviation can be calculated across various private datasets without compromising privacy. It operates similarly to federated learning in that it runs local calculations over each client device's data and only makes the aggregated findings -- never any data from a specific device -- available to developers. Sensitive data like medical records, financial transactions, employee data, and others can be analyzed without leaving the premise.

The current release of Raven Distribution Framework (RDF v0.3)provides an easy to use library that allows developers to build mathematical algorithms or models and computes these operations by distributing them across multiple clients. This provides an increase in speed and efficiency when dealing with a large number of mathematical operations. Distributed Computing is the linking of various computing resources like PCs and smartphones to share and coordinate their processing power for a common computational requirement, such as the training of a large Machine Learning model. These resources or nodes communicate with a central server and in some cases with each other, such that each node receives some data and completes a subset of a task. These nodes can coordinate their computations to complete a large and complex computational requirement in a fast and efficient manner.