3rd IEEE P3652.1 Federated Machine Learning Working Group Meeting, a Joint Effort Facilitating and Accelerating Industry Standard-Setting

MACAO, Aug. 16, 2019 /PRNewswire/ -- The curtains fell on the 3^rd IEEE P3652.1 (Guide for Architectural Framework and Application of Federated Machine Learning) Working Group Meeting in Macao, on 11^th August. Federated Machine Learning is the key pioneering solution to the data privacy protection conundrum in the AI sector. The 3^rd meeting built on classic case studies in the previous two meetings, to formulate a Draft of Standards to be launched by 2020. Joining the event are 22 renowned enterprises and research institutions namely WeBank, Clustar, Sinovation Ventures, JD iCity, 4Paradigm, Squirrel AI, Eduworks, Tongdun, BGI, LogiOcean, GSTA, Swiss Re, Intel, Alibaba, China AMC, Huawei, Senses Global, Dareway, Doc.ai, CETC Big Data, NTU (Nanyang Technological University) and HKUST (The Hong Kong University of Science and Technology).

Launched by WeBank, IEEE P3652.1 Program is the first in the world dedicated to standard-setting for Collaborative AI Technology Framework. The 3^rd meeting, hosted by Clustar, focused on quantifying the indicators, ensuring standards serve the compliance of technologies, as well as classifying and summarizing use cases of Federated Learning.

"Standard-setting serves as an important backbone for the healthy and orderly development of technologies. This meeting boosted the standard-setting progress for Federated Learning significantly," said the Chair of the IEEE P3652.1 Federated Machine Learning Working Group and Chief AI Officer of WeBank Professor Qiang Yang. "All parties should fully leverage their strengths in improving standards within industries that help promote Federated Learning."

So far, over 30 renowned research institutions and businesses from the technology, finance, education, healthcare sectors are contributing to the IEEE P3652.1 Program as members of the working group and that number is mounting. On top of the visionary and authoritative agenda for discussion, the pooling of know-how in technology, R&D, service and abundant experience in operation by all parties offer valuable thoughts for reference.

In June, AIOSS launched the Team Standard Reference Architecture of Information Technology Service for Federated Learning, the first team standard for Federated Learning initiated by WeBank. In the future, the launch of IEEE P3652.1 will further promote the application of Federated Learning in all sectors by providing technical standards and basis for cooperation across industries. Given that countries and regions have different emphasis regarding the demand for supervision in data privacy and security, all parties will continue to hold discussions through remote communication, to formulate a set of technology standards applicable worldwide.

SOURCE IEEE P3652.1 Federated Machine Learning Working Group