Qiang Yang
                        
                                        
                        
    
    
            
            
            
                                                                
    
                    
                
                    
    
    
                
    
                    
            
                
            
            
                                                    
    
                    
                
                    
    
    
                
    
                    
            
                
            
            
                                                    
    
                    
                
                    
    
    
                
    
                    
            
                
            
            
                                                    
    
                    
                
                    
    
    
                
    
                    
            
                
            
            
                                                    
    
                    
                
                    
    
    
                
    
                    
            
                
            
            
                                    
            
        
                                                
                Federated Learning
Ebook (EPUB Format)
            How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private?Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions…
        
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                                    Beschreibung
                        How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private?Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. 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. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
                    
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Produktdetails
Weitere Autoren: Liu, Yang / Cheng, Yong / Kang, Yan / Chen, Tianjian / Yu, Han
- ISBN: 978-1-68173-718-8
- EAN: 9781681737188
- Produktnummer: 33415960
- Verlag: Morgan & Claypool Publishers
- Sprache: Englisch
- Erscheinungsjahr: 2019
- Seitenangabe: 207 S.
- Plattform: EPUB
- Masse: 12'069 KB
- Auflage: 1. Auflage
Über den Autor
            Qiang Yang is the head of the AI department at WeBank (Chief AI Officer) and Chair Professor at the Computer Science and Engineering (CSE) Department of the Hong Kong University of Science and Technology (HKUST), where he was a former head of CSE Department and founding director of the Big Data Institute (2015-2018). His research interests include AI, machine learning, and data mining, especially in transfer learning, automated planning, federated learning, and case-based reasoning. He is a fellow of several international societies, including ACM, AAAI, IEEE, IAPR, and AAAS. He received his Ph.D. in Computer Science in 1989 and his M.Sc. in Astrophysics in 1985, both from the University of Maryland, College Park. He obtained his B.Sc. in Astrophysics from Peking University in 1982. He had been a faculty member at the University of Waterloo (1989-1995) and Simon Fraser University (1995-2001). He was the founding Editor-in-Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and IEEE Transactions on Big Data (IEEE TBD). He served as the President of International Joint Conference on AI (IJCAI, 2017-2019) and an executive council member of Association for the Advancement of AI (AAAI, 2016-2020). Qiang Yang is a recipient of several awards, including the 2004/2005 ACM KDDCUP Championship, the ACM SIGKDD Distinguished Service Award (2017), and AAAI Innovative AI Applications Award (2016). He was the founding director of Huawei's Noah's Ark Lab (2012-2014) and a co-founder of 4Paradigm Corp, an AI platform company. He is an author of several books including Intelligent Planning (Springer), Crafting Your Research Future (Morgan & Claypool), and Constraint-based Design Recovery for Software Engineering (Springer).
        
                                        
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