The latest news of FedAI are updated! Let’s find out what happened in October.

FedAI Admin

Hi All,


The latest news of FedAI has been released. Please find out what happened in October.


1.          WeBank’s AI group won the “2019 CCF Science Technology Award”. Federated learning builds a benchmark in the AI industry.


The list of 2019 CCF Science Technology Award is published. By “the research and application of federated learning technology system” project, WeBank’s AI team won the “Science and Technology Progress Award of 2019 China Computer Federation (CCF) Science Technology Award”, which represents the highest recognition in the field of computer science in China.


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2.          Focusing on the demand classification and security evaluation of the federated learning scenario, the IEEE Federated Learning Standard Working Group holds the 4th meeting.


Recently, the 4th IEEE P3652.1 (Guide for Architectural Framework and Application of Federated Machine Learning) Working Group Meeting was held successfully in Beijing. The Peking University, IEEE, WeBank, Sinovation Ventures, JD, China Telecommunications Corporation, Tencent, Xiaomi, Alibaba, YiTu, Clustar, 4Paradigm, Huawei, VMWare, LogiOcean, SensesGlobal, Swiss Re, Intel, CETC BigData, Ant finance, China Asset Management, Fudata, total 22 top companies and research institutions participate in the meeting. The meeting focuses on the demand classification and security evaluation of federated learning scenario and planning the security evaluation and rating of federated learning to further discuss the standard-setting progress for federated learning.


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3.          From discussing the basic theory to practical application, the 23rd CCF TF workshop presents the new generation application case of federated learning


On October 26th, China Computer Federation (CCF) holds the 23rd CCF TF workshop about “the latest case of federated learning application” in Beijing. Representatives from WeBank, Tencent, Huawei, Ping An and other top companies and universities jointly discuss and present the new generation case of federated learning applying in all industries. About 150 people come from top ICT and internet companies, including students from key universities, also join to discuss this topic.


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4.          WeBank appears in CV101 and explains the new achievement of federated learning in the visualization field.



Recently, the “Computer Vision Youth Developer Technology and Application Conference & Developer List Awards Ceremony” was held in Shenzhen. A lot of experts and the representatives of companies from the AI field were invited to attend the conference to jointly discuss the latest technology and future trends. The deputy general manager of WeBank’s AI department, Tianjian Chen gave his speech “the principle and expectation of federated visual system”.


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5.          Federated learning applies to smart retail. WeBank’s AI group helps to Mei Cai seize the opportunity in digital transformation.


On the 25th of October, the 2019 China Catering Supply Chain Conference was held in Beijing. About 1000 representatives from the head enterprises and universities discuss the current situation of the catering supply chain in the new era, the opportunities for development and the upgrade direction of future innovation. WeBank’s AI group is the representative of the implementation of AI application to solve the pain points from the fresh-food retail industry. In the conference, senior researcher Minghua Zheng shared the idea of “AI Selling prediction” and “insight system of business opportunities”. WeBank will introduce financial AI into the entity economy with Mei Cai, the conference organizer, to jointly improve the current situation of the fresh-food retail industry.


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6.          The FATE version 1.1 is updated. Seven highlights of FATE help AI build models efficiently.

FATE is the world's first industrial-scale open-source framework. It aims to provide strong support for the federated learning architecture system and security computing of different machine learning. On the 31st of October, FATE v1.1 has been updated with seven highlights as follows.

* Provide a general algorithm framework supporting secure aggregation for homogeneous federated learning

* Add Homogeneous Deep Neural Network, Heterogeneous Linear Regression and Heterogeneous Poisson Regression

* Support multi-host heterogeneous federated learning

* Add Spark as computing engine

* Add service governance of FATE-Serving

* Add Heterogeneous SecureBoost online inference of FATE-Serving

* KubeFATE provides fully containerized cloud native deployment


Thanks for supporting and using FATE. In the future, we will update the news monthly about the events of FATE. Let us jointly build the federated learning ecosystem!


Best regards

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