Chair: Qiang Yang,  qyang@cse.ust.hk

Vice Chair: Ji Feng, fengji@chuangxin.com

Secretary: Ya-Ching Lu, angelica.lv@clustar.ai

Treasurer: Junbo Zhang, msjunbozhang@outlook.com

Scope

Federated learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across data owners. This guide provides a blueprint for data usage and model building across organizations while meeting applicable privacy, security and regulatory requirements. It defines the architectural framework and application guidelines for federated machine learning, including 1) description and definition of federated learning, 2) the types of federated learning and the application scenarios to which each type applies, 3) performance evaluation of federated learning and 4) associated regulatory requirements.

Purpose

The European Union’s General Data Protection Regulation and similar government actions bring new challenges to the big data and artificial intelligence (AI) community. Many normal operations in the big data domain, such as merging user data from various sources to build an AI model, are, without explicit permission from all users, are to be considered illegal in the new regulatory framework. The purpose of this guide is to provide a feasible solution for the industrial application of AI — using data collectively without exchanging data directly. This guide is expected to promote and facilitate collaborations where privacy and data protection issues have become increasingly important. This guide will promote and enable to use of distributed data sources for the purpose of developing AI without violating regulations or ethical considerations.

Membership

Membership is entity based and an individual cannot become a member. Membership requires IEEE-SA Advanced Corporate Membership to become a member. For further information on IEEE Corporate membership and dues please see go here