Press Release
Press Release After over a year of preparation and restructuring, the decentralized and privacy-enabled AI organization Federated Learning Consortium ( FLC will be led by founding key members blockchain technology platform Phoenix and leading China-based consumer data and AI company APEX Technologies will lead the effort.
The vision of the FLC focuses on the research, development, and dissemination of cutting-edge technologies surrounding collaborative AI, including collaborative learning, blockchain-enabled AI, MPC (multiparty computing), and TEE (trusted execution environment). In particular, the organization will be interested in combining deep learning techniques for large data sets, such as reinforcement learning, with high-performance infrastructure using GPU computing in a distributed and federated approach.
Membership in the organization will be open to AI-related technology companies, blockchain companies, and system integrators. The goal is to be able to provide holistic, implementable, and high-performing solutions to the broader market, with an initial focus on China and Asia. Through internal partnerships and collaborative research projects, companies will be able to offer new technology solutions that would not have been possible on their own
The FLC is also open to individual memberships for academics and industry professionals. Currently, the FLC already has initial members who are experts in machine learning and federated learning from leading Chinese companies such as HuaAT (华院数据), FuData (富数科技), and Tencent.
FLC will focus on developing technology solutions for a variety of different verticals, including retail, financial services, automotive, asset management, IoT, and government.
For more information
FLC: https://flc.ai/
Phoenix https://phoenix.global/
APEX Technologies https://www.apextechnologies.com/
This is a press release. Readers should exercise their own due diligence before taking any action related to the advertised company or its affiliates or services. or for any damage or loss caused or alleged to be caused, directly or indirectly.
Image credits: Shutterstock, Pixabay, Wiki Commons