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VMware Donates FedLCM Project to LF AI & Data Foundation to Strengthen the Operationalization of Federated Learning Platform

By October 27, 2022No Comments

VMware, a leading provider of multi-cloud services for all applications, today announces plans to donate the FedLCM (Federation Lifecycle Manager) open source project to FATE, an Incubation-stage project under the umbrella of the LF AI & Data Foundation. The FedLCM project aims to streamline the provisioning and management of federated clusters.  

After the donation, FedLCM will work to operationalize the lifecycle of federated learning platforms. FATE is one of the largest communities in the federated learning space and has 4000+ members from enterprises and research institutes around the world. The vision of the community is “open source, open community, and co-innovation”. VMware is a board member of the technical steering committee (TSC) of FATE.   

“We’re thrilled to welcome FedLCM to our community,” said Ibrahim Haddad, Ph.D., General Manager of LF AI & Data. “FATE just joined our portfolio of projects with a strong community presence. The momentum demonstrated by the donation of FedLCM only builds on the progress made by FATE. Both of these initiatives align with our mission of building and supporting an open AI and data community while driving innovations with collaborations with community members.

“We are very excited to open source and donate FedLCM as an important tool to the federated learning community.” said Henry Zhang, the Chair of FATE’s Development Committee, Engineering Director of Cloud Native Lab, VMware R&D,  “It is a long-awaited project which provides the powerful capabilities to manage the lifecycle of federated learning frameworks such as FATE and OpenFL. We will continue to collaborate with community members to improve FedLCM and drive its wider adoption.”

About the FedLCM Open Source Project 

Federated learning (also known as collaborative learning) is a machine learning technique that trains a model across multiple organizations holding local data samples, without exchanging them. Orchestrating the federated learning tasks while maintaining the data privacy and security is challenging. The FedLCM project reduces the barriers to using the federated learning platform by providing neutral support to multiple federated learning frameworks. It offers a unified experience to provision and manage different frameworks across organizations in a federation. Now many industries that employ AI technology face new problems – such as “data silos,” where data is dispersed across isolated sources, and a growing concern about “data privacy”. FedLCM supports multiple infrastructures such as Kubernetes. The federated learning frameworks deployed in these infrastructures can collaborate as a federation to break data silos without compromising data privacy. 

FedLCM comes with a site management platform – the Site Portal for FATE. The Site Portal is a graphical federated learning task management service that allows users to initiate or join a federal learning task through a web GUI. 

For more information about FedLCM, or to contribute to the project, please visit GitHub.  

About FATE

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. FATE is currently hosted under LF AI & Data Foundation

FATE Key Links

LF AI & Data Resources