Federated Learning Consortium (FLC) for Decentralized AI to Launch in Hong Kong, Led by Phoenix and APEX Technologies
PRESS RELEASE. After over a year of preparation and restructuring, decentralized and privacy-enabled AI organization Federated Learning Consortium (FLC) is set to launch as a for-profit research consortium in Hong Kong, China, shifting from a previously non-profit approach. FLC is set to be led by founding keystone members – blockchain technology platform Phoenix and leading China-based consumer data and AI company APEX Technologies.
FLC’s vision is centered on researching, developing, and promoting cutting-edge technologies surrounding federated AI, including federated learning, blockchain-enabled AI, multi-party computation (MPC), and TEE (trusted execution environment). The organization will be particularly interested in combining large-dataset deep learning technologies, such as reinforcement learning, with highly performant infrastructures using GPU computing using a decentralized/federated approach.
Organizational membership will be open to AI related technology firms, blockchain firms, and system integrators – the goal is to be able to provide holistic, implementable, and highly performant solutions for the broader market, focused initially on China and Asia. Through internal partnerships and joint research projects, organizations will be able to deliver new technology solutions that were not possible on a standalone basis,
Individual membership is also available for academics and industry experts. Currently FLC already has an initial roster of machine learning and federated learning experts from leading China-based firms such as HuaAT (华院数据), FuData (富数科技) , and Tencent.
FLC will be focused on developing technology solutions for various different verticals, including but not limited to 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/
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