LF AI & Data Foundation—the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI) and data open source projects, today is announcing FATE as its latest Incubation Project.
Dr. Ibrahim Haddad, Executive Director of LF AI & Data, said: “We’re excited to welcome the FATE project in LF AI & Data. The project will benefit our mission of building and supporting an open AI and data community, and driving the AI & data innovations with collaborations with community members. FATE already has some integrated workflow with the KServe project and is also looking at other collaboration opportunities with more projects in the AI & Data field and technical integration with hosted projects . We look forward to working with the community to grow the project and create new collaborations with our members and other hosted projects.”
Released and open sourced by WeBank, FATE (Federated AI Technology Enabler) is the world’s first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.
“We are thrilled that FATE project joins LF AI & Data.” said Prof. Qiang Yang, TSC Chair of FATE, Fellow of AAAI, ACM, Royal Society of Canada (RSC) and Canadian Academy of Engineering (CAE), Chief Artificial Intelligence Officer of WeBank, Chair Professor of CSE Department at Hong Kong University of Science and Technology (HKUST), “The FATE project leads a technological wave of trustworthy federated learning and has been proven mature and successful by many users to solve their challenges in applications of data element. The FATE project will benefit greatly from the thriving user community of LF AI & Data.”
Federated learning is one of the most promising ML technologies to help overcome data silos — strengthening data privacy and security — while still complying with laws and regulations, such as General Data Protection Regulation (GDPR). FATE enables the production-level application of federated learning in many industries including smart cities’ security, finance, medicine, etc.
The goals and values of FATE project are to further productize federated learning applications and build a FederatedAI ecosystem where community members can use this trusted, open-sourced platform to fulfill their real-world business needs while complying with all the privacy and security regulations and requirements. It is designed to be easily extensible so that people can experiment, implement and deploy their federated learning algorithms and ideas rapidly. Due to the nature of project FATE and federated learning, data and related processing techniques are also one of the key focuses where many innovations happen. FATE already has some integrated workflow with the KServe project and is also looking at other collaboration opportunities with more projects in the AI & DATA field.
LF AI & Data supports projects via a wide range of services, and the first step is joining as a Sandbox Project. Learn more about FATE on their GitHub and join the FATE-Announce Mailing List and FATE-Technical-Discuss Mailing List.
A warm welcome to FATE! We look forward to the project’s continued growth and success as part of the LF AI & Data Foundation. To learn about how to host an open source project with us, visit the LF AI & Data website.
FATE Key Links
- Mail Lists:
LF AI & Data Resources
- Learn about membership opportunities
- Explore the interactive landscape
- Check out our technical projects
- Join us at upcoming events
- Read the latest announcements on the blog
- Subscribe to the mailing lists
- Follow us on Twitter or LinkedIn
- Access other resources on LF AI & Data’s GitHub or Wiki