Federated Analytics is a step forward in data analysis, allowing data from different decentralized sources to be analyzed without having to be combined or moved into a centralized system. The data remains in its original location, and machine learning algorithms are applied to it there. This enables a broad analysis across datasets without ever exposing the raw data, ensuring data privacy and security. It's typically applied in fields where sensitive data is involved, such as healthcare or financial services.
The concept of Federated Analytics came into light with the rise of 'Federated Learning' in the late 2010s, inspired by Google's paper "Communication-Efficient Learning of Deep Networks from Decentralized Data" published in 2017. The concept grew in recognition and usage as concerns about data privacy intensified globally.
Google remains a pioneering figure in the development and practical application of Federated Analytics following their influential research paper. In addition, several other tech companies and academic institutions have contributed to its evolution, further integrating it with newer AI technologies.