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  What is Federated Learning? An introduction to machine learning techniques that have been discussed recently   In the book "No More Theorizing: Hands-on Engineering Project Implementation of Federated Learning", while explaining the basic principles, it also provides a description of the actual application practice. Taking the commercial application of China's financial scene as an example, it describes the specific practice in detail The process provides a general outline for readers who are new to federated learning. The concepts and practices of data trading and sharing mentioned in this book can also provide reference for the practical application of federated learning in China. The following are excerpts from the book: Data element transaction based on federated learning   The Background and Current Situation of Data Element Trading   Data has been recognized as a basic strategic resource and a key factor of production, a basic resource for economic and social development, and an engine for a new round of technological innovation. Digital transformation is a key factor to promote industrial upgrading, and to achieve digital transformation, a very important aspect is to realize the optimal setting of data assets. However, the current ubiquitous data distribution imbalance and "data island" problems directly lead to the inability to fully express the huge value of data. With such a situation, a huge market demand for data sharing is naturally born. Of course, data sharing does not happen overnight. In the process of data sharing, there are still many problems to be solved, including the confirmation of data ownership, the delineation of data rights boundaries, the unclear rules for the distribution of rights and interests, and the lack of data security. It is of great practical significance to formulate reasonable data sharing specifications, use technical means to ensure data security, and solve problems such as data ownership confirmation and use boundaries, in order to promote legal data sharing in compliance with regulations, and the efficient and high-quality development of the financial industry. The driving force behind data sharing is the value of data. Since value is involved, the process of sharing must be accompanied by the pricing and transaction process of data as an element. Such pricing and trading is an important mode to realize data sharing, and dozens of data trading platforms have emerged so far. From some data trading platforms with great influence in China (below), we can see that the current data trading platforms mainly include third-party data trading platforms and comprehensive data service platforms. Among them, the third-party data trading platform mainly provides services such as data asset transactions, inquiries, and demand releases. In addition to these services, the comprehensive data service platform also often provides some technical services such as data exploration modeling and online operation of models. The data sources and fields of the data trading platform also cover a wide range. The data sources include government public data, data provided by data providers, internal enterprise data, web crawler data, Internet open data, etc. The fields include government affairs, economy, Transportation, communication, commerce, agriculture, industry, environment, medical treatment, etc. The ways to provide data services or products include API, data suites, data products, data customization services, solutions, etc.    

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