The Linux Foundation has chosen to host a new federated learning framework that will help advanced data modelling through artificial intelligence (AI).
The Federated AI Technology Enabler (FATE) will promote collaboration between organisations and institutes to carry out AI model training and inference, in accordance with user privacy, data confidentiality, and government regulations.
FATE is an open source project initiated by Chinese digital bank 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.
"We aim to build a Federated AI ecosystem based on federated learning and transfer learning, so that anyone can fully enable their data values and promote innovative applications," says
FATE project advocate and deputy general manager of WeBank AI Group, Tianjian Chen.
"The Linux Foundation is the leader in neutral homes for this kind of work to flourish, grow and become the industry standard."
The Linux Foundation says there are many challenges when developing responsible AI models – these include safety, fairness and data protection. FATE allows developers and data scientists to work collaboratively across different data controllers to advance AI models in a trustworthy way that protects data by design and adheres to government requirements, such as GDPR.
"A secure computing framework is critical for developers who are using data and models to build the latest applications across financial services, manufacturing, healthcare and more," explains The Linux Foundation executive director Jim Zemlin.
"It is exactly this kind of work that is a natural fit for the support of the Linux Foundation and the global open source community."
FATE also provides a series of toolkits to address semi-black box experimentation, secure computation cost and lifecycle management issues of federated AI models.
For the current issues of "small data" and "data silos" that exist in most application scenarios, FATE, as an industrial-level federated learning framework, provides a comprehensive solution to solve these issues," adds CETC Big Data Research Institute's general technical research centre deputy director Dr. Xu Cheng.
"It can meet the requirement of data joint modelling and usage in the condition of satisfying security compliance, and further, expand and deepen the sharing and openness of government data."
4Paradigm, CETC Big Data Research Institute, Clustar, JD Intelligent Cities Research, Squirrel AI Learning, Tencent and WeBank are among the first organizations committed to the new Foundation.