Federated Learning Consortium (FLC) for Decentralized AI to Launch in Hong Kong, Led by Phoenix and APEX Technologies

PRESS RELEASE. After more than a year of preparation and restructuring, the decentralized and privacy-enforced AI organization Federated Learning Consortium (FLC) is set to launch as a commercial research consortium in Hong Kong, China, moving away from a previously non-commercial approach. The FLC will be led by the founders of Keystone, blockchain technology platform Phoenix, and China’s leading data processing and artificial intelligence company, APEX Technologies.

FLC’s vision is focused on the research, development, and advancement of advanced technologies related to federated AI, including federated learning, blockchain-enabled AI, Multi-Party Computing (MPC), and TEE (Trusted Execution Environment). The organization will be particularly interested in combining deep learning technologies with large datasets, such as reinforcement learning, with high-performance infrastructures using GPU computing using a decentralized/federated approach.

Organizational membership will be open to AI firms, blockchain firms and system integrators – the goal is to be able to provide holistic, implementable and high performance solutions to a broader market initially focused on China and Asia. Through internal partnerships and joint research projects, organizations will be able to offer new technological solutions that would not be possible on their own.

Individual membership is also available for academics and industry experts. At present, FLC already has an initial roster of machine learning and federated learning experts from leading Chinese firms 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 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 technology: https://www.apextechnologies.com/

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