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 OpenFL as its latest Incubation Project.
Dr. Ibrahim Haddad, Executive Director of LF AI & Data, said: “We are thrilled to welcome OpenFL as our newest Incubation project, promoting open source innovation in AI and Data. As Federated Learning becomes increasingly crucial, OpenFL’s mission aligns with our goal to build a collaborative environment for developing cutting-edge technologies. We support OpenFL’s mission to democratize machine learning while preserving data privacy. Our commitment to transparency, inclusivity, and innovation will fuel a successful partnership, driving progress and transforming the industry. We look forward to enabling integration opportunities with other hosted projects in LF AI & Data and supporting OpenFL’s growth and its community.”
OpenFL is a Python 3 library for federated learning that enables organizations to collaboratively train or validate a model without sharing sensitive information.
Federated learning is a decentralized approach to machine learning that allows for cooperation on projects without disclosing sensitive information, such as confidential patient data, financial information, or classified secrets (Pati S, et al., 2022; Reina A, et al., 2021; Sheller MJ, et al., 2020). In federated learning, instead of moving private data to a central location to train the model, the model is sent to the private data for training or validation. This enables access to private data that can improve both the volume and diversity of datasets thereby mitigating model bias and improving its generalizability.
OpenFL can work with any framework, including TensorFlow or PyTorch.
The OpenFL Project aims to develop a Federated Learning library that is flexible, secure, scalable, and easy for data scientists and owners. OpenFL enables decentralized training and validation of AI models across data silos, which speeds up the deployment of Federated Learning and grants greater access to data while preserving privacy and security.
Prashant Shah, Global Head of AI for Intel Health and Life Sciences and member of the OpenFL technical steering committee said: “The field of privacy-preserving machine learning is evolving rapidly. LF AI & Data Foundation provides OpenFL with a neutral home and a robust AI developer community to drive rapid innovation through increased adoption of federated learning and evaluation of AI models on private datasets. This continues Intel and Linux Foundation’s long partnership in AI and beyond.”
The OpenFL project aims to establish a robust ecosystem that standardizes the concept of Federated Learning across industries. Furthermore, the project aims to transition from “Intel Open Source” to a neutral 3rd-party community-based foundation that allows other ecosystem players to collaborate, contribute, and utilize the open-source FL framework. OpenFL is excited to become a part of the expanding ecosystem of LF AI & Data and actively contribute to an open community for AI.
LF AI & Data supports projects via a wide range of services, and the first step is joining as a Sandbox Project. Learn more about OpenFL on their GitHub and join the OpenFL-Announce Mailing List and OpenFL-Technical-Discuss Mailing List.
A warm welcome to OpenFL! We are excited to see the project’s continued growth and success as part of the LF AI & Data Foundation. If you are interested in hosting an open source project with us, please visit the LF AI & Data website to learn more.
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