Overview

Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in the following article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.

@article{elkordy2023federated,
  title={Federated analytics: A survey},
  author={Elkordy, Ahmed Roushdy and Ezzeldin, Yahya H and Han, Shanshan and Sharma, Shantanu and He, Chaoyang and Mehrotra, Sharad and Avestimehr, Salman and others},
  journal={APSIPA Transactions on Signal and Information Processing},
  volume={12},
  number={1},
  year={2023},
  publisher={Now Publishers, Inc.}
}

TensorOpera®Federate Federated analytics (FA) is the industrial grade library of cross-silo federated analytics for cross-organization/account analytics. Researchers and engineers do not need to maintain the complex geo-distributed clusters. Essentially, Our MLOps can seamlessly migrate the local development to the real-world edge-cloud deployment without code changes.