Security & Compliance
Meet security and
Ensure data privacy and
Joint & Expansion
Connect with business partners
of various industries;
Exploit and extend
the value of data
Promotion & Empowerment
Sustainable and intelligent
Stable and win-win
No data leakage to the outside
Federated learning preserves model quality
Equivalence of participants status
Mutual benefits for participants
Federated learning is a machine learning framework that helps multiple organizations effectively and collaboratively use data and modeling in accordance with user privacy protection, data security, data confidentiality and government regulationsWhite Paper
Hankz Hankui Zhuo, Wenfeng Feng, Qian Xu, Qiang Yang, Yufeng Lin
Internet finance platforms, micro loan companies and banks which are qualified for small business loans, may collaboratively explore a federated learning framework for risk management and share the final prediction model with participating organizations.
Ensure Data Safety and No Data Leakage
Improve Model Performance and Mutual Benefit
Financial institutions may not have enough money laundering cases to build an efficient machine learning model. Through federated learning framework, they can collaboratively build a detection model without sharing their data, improve accuracy and efficiency of the anti money laundering system.
Meeting Requirements on Financial Data Security and Compliance
Joint Modeling to Solve Few Labels Problem
The federated modeling can guarantee the data security between different equipment operators, overcome the key issues of less labels and low prediction accuracy of single equipment, and improve anomaly detection accuracy and operational efficiency globally.
Improve The Anomaly Detection Accuracy
Enhance Operational Efficiency and Reduce Costs
Medical institutions do not share their data, and users are reluctant to disclose personal disease data. The application of federated learning on medical images can solve the problem of medical data isolation and improve AI applications in the computer-aided diagnosis field.
Enhances The Ability of Medical Institutions to Applying Data
Federated Multi-party Data and Improve Detection Key Metrics
FATE is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.
China’s first web-only bank hopes A.I. and robots can improve customer service
Analysts advise widespread AI use
How to solve the big data plight of AI? federated transfer learning
The Federated AI Ecosystem project is initiated by WeBank's AI Department. WeBank is a technology driven internet bank based in Shenzhen, China. The team's aim is to develop and promote advanced AI technology that preserves data security, data confidentiality and user privacy.
"Build a Federated AI Ecosystem based on federated learning and transfer learning. So that our partners can fully exploit their data values and promote vertical applications. It can carry on AI empowerment to related entities, and enhance their modeling technologies and abilities"
Open Source Technology
Accelerate the open-sourcing of federated transfer learning, and develop more applications
Standards and Guidelines
Formulate architectural framework and application guideline of federated learning, and facilitate industry cooperation
Multi-Party Consensus Mechanisms
Encourage more institutions to participate and establish a multi-party consensus mechanism based on technologies like blockchain
Vertical Fields Applications
Focused on vertical field data and scenarios, built a new business model and ecology
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