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Introducing FATE 1.0: Milestone Version Introduces New Features, Stability and Performance Enhancements

By August 22, 2019No Comments

This is a guest blog post by the FATE community, a Linux Foundation member with commonality of interests with LF AI

The FATE community is excited to announce the availability of FATE 1.0. We are striving to improve the development of federated learning technologies to achieve more powerful functions and applications. We consider FATE 1.0 to be a milestone version which empowers the FATE community with more powerful tools and a significantly improved developer experience.

FATE (Federated AI Technology Enabler) is a federated learning framework that fosters collaboration across companies and institutes to perform AI model training and inference in accordance with user privacy, data confidentiality and government regulations.

FATE recently joined the Linux Foundation with several organizations supporting the project including 4Paradigm, CETC Big Data Research Institute, Clustar, JD Intelligent Cities Research, Squirrel AI Learning, Tencent and WeBank.

What’s new in the FATE 1.0 release

  • FATEBoard, a visual tool for federated learning modeling for end-users
  • FATEFlow, an end-to-end pipeline platform for federated learning
  • Performance updates for all algorithm modules
  • Mature features of online federated inference

FATE 1.0 benefits and features

  • FATEBoard visualizes the federated learning process
    • Greatly improving the federated modeling experience, FATEBoard allows end-users to explore and understand models easily and effectively
    • FATEBoard supports visualization in the status changes of training, model graphs, logs tracking, and much more, which makes federated learning modeling easier to understand, debug and optimize
    • Click here for more information
  • FATEFlow builds highly flexible, high performance federated learning pipeline production service
    • FATEFlow supports model life-cycle management functions, which implement state management of pipelines and the collaborative scheduling of operation, and automatically tracks data, models, metrics, and logs generated in the task to facilitate analysis of users
    • Learn more or get started here
  • Performance Updates provide high flexibility, high stability and high performance for federated learning
    • FATE 1.0 supports use of DSL to describe federated modeling workflow
    • FATE 1.0 introduces a new Homomorphic Encryption algorithm based on Affline Transforms
    • FATE 1.0 also supports the Nesterov Momentum SGD Optimizer, which makes the federated learning algorithm converge quickly

Getting Started

FATE supports three deployment modes: Standalone in Docker, Standalone Compiled, and Cluster Compiled. The Cluster in Docker mode is expected to come with the next release. Stay tuned by joining the Fate-FedAI mailing-list, or you can also visit the FATE README

Suggestions or Contributions

Join us in our community via regular meetings, or our mailing-list and give us your feedback.

Anyone interested in federated learning is welcome to contribute code and submit Issues or Pull Requests. Please refer to the FATE project contribution guide first.

About the FATE Project

FATE is an open-source project initiated by WeBank’s AI Group to provide a secure computing framework for building the federated AI ecosystem. It implements a secure computation version of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. For developers who need more than out-of-box algorithms, FATE provides a framework to implement new machine learning algorithms in a secure MPC architecture. Learn more about the project at


  • Andrew Bringaze

    Andrew Bringaze is the senior developer for The Linux Foundation. With over 10 years of experience his focus is on open source code, WordPress, React, and site security.