In the big data and AI era, how to effectively utilize the decentralized data? How to address data security and enabling privacy? The first monograph on federated learning- <Federated Learning> gives you answers.
<Federated learning> is from the renowned AI-series published by Morgan & Claypool Publishing House, written by six leading AI experts in two years. This monograph shares the experience and insights of the WeBank AI group in the field of federated learning in promoting privacy-preserving AI and collaborations among different organizations.
What does the monograph tell us?
In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. Federated learning provides a feasible solution that the data owners can share the machine learning models while ensuring that no local data leaves any data owners. This book presents recent advances in federated learning research, open-source platforms and potential applications, as well as examples of practical use cases of federated learning in finance and computer vision, etc. This monograph shows that federated learning is a fundamental building block of the next generation AI. It enables large-scale collaboration among geo-distributed data owners.
The content of this book:
- The overview of federated learning: problems to be solved, definition, classification, development, etc.;
- Background knowledge of federated learning, including the privacy protection of machine learning technology and data analysis;
- Introduction of distributed machine learning. Emphasizing the difference between federated learning and distributed machine;
- The definition, framework, algorithm of horizontal federated learning, vertical federated learning, and federated transfer learning
- Incentive mechanism design based on economic principles and game theory
- The practical use cases of federated learning in computer vision and Natural language processing and recommendation systems
- Federated reinforcement learning
- The potential applications of federated learning in different industries, such as finance, medical treatment, education, smart city
What can you learn from this book?
We are in an era with massive but fragmented data. We need a solution to utilize the scattered data and produce added-values, while ensuring user privacy and data security. Federated learning provides a feasible solution for building AI using decentralized data and for protecting user privacy and data security. The practice of federated learning is the new dawn of AI.
This book provides with the readers a new perspective about building privacy-preserving AI, especially for those who are concerned with data abuse and user privacy in the big data and AI industry.
Please click here for purchasing the English version! The Chinese version will be available in April. Coming soon!
About WeBank AI Group
This monograph is written by WeBank AI group leading by the chief artificial intelligence officer (CAIO) Qiang Yang. Prof. Qiang Yang is the pioneer of transfer learning and federated learning. He is the first Chinese scholar who became a fellow of the Association for the Advance of Artificial Intelligence (AAAI) and a member of the AAAI executive committee. Prof. Qiang Yang is the chairman of the council of the International Joint Conference on Artificial Intelligence (IJCAI), the first Chinese scholar to serve as the chairman. He won the 9TH WU WEN JUN AI OUTSTANDING CONTRIBUTION AWARD in 2019, which is a very prestigious award in the area of AI in China.
The WeBank AI group is a top AI research team of WeBank. It aims to use the autonomous and controllable, safe and dependable AI technology to explore the new way of FinTech, leading the new direction of the AI industry. We have achieved great successes in the following four fields: The FedAI ecosystem, new generation human-computer interaction, precision marketing, and smart asset management.
In the field of federated learning, the WeBank AI Group have initiated the establishment of IEEE standard and domestic standard on federated learning. The WeBank AI Group have also released the world’s first industrial-grade open-source federated learning platform, known as FATE (Federated AI Technology Enabler). We keep exploring possible business opportunities and applications of federated learning, and seeking to solve practical problems. We endeavor to promote the FedAI ecosystem and welcome collaborations from both academic and industrial partners. We try to build trustworthy and responsible AI under data protection laws and regulations.