Category: General Catalog of "In-depth Understanding of Federated Learning"
There is no doubt that we are now experiencing the fourth information revolution of the Internet, with massive amounts of information and data. If these data can be interpreted in an AI way, it will bring subversive changes to human daily life. As the underlying technology of future AI development, federated learning relies on the model of connecting data islands under safe and reliable data protection measures, and will continue to promote the innovation and leap of global AI technology. With the penetration and application of federated learning in a wider range and more industry scenarios, it will have a huge impact on various groups of people, organizations, industries and society at a higher level. The public value of federated learning is mainly reflected in the following aspects Aspects:
- Accelerate the innovation and development of artificial intelligence technology: artificial intelligence technology has now formed an industrial ecology that gathers multiple resources such as global technology, capital, talents, and influence. As an indispensable core technology at the bottom of AI modeling, federated learning will truly help large-scale The value of data is realized, and the deep integration of various fields of AI in various industries is driven under the environment where the data does not come out of the local area, so that artificial intelligence technology can clear away data obstacles and continue to grow and innovate iteratively.
- Guaranteeing private information and data security: Federated learning can ensure that the individual's own data does not leave the local area. The federated system establishes a virtual shared model through the parameter exchange method under the encryption mechanism without violating data privacy protection regulations. When building a virtual model, the data itself does not move, nor does it leak user privacy or affect data specifications, which fully guarantees individual privacy information and data security.
- Promote the improvement of the intelligence level of the whole society: AI technology based on federated learning will be more safely integrated into social infrastructure and life. It can not only assist human work and life, but also gradually change human cognitive models, thereby promoting social economy and develop.
Federated learning technology is a "cooperative win-win" model that is extremely valuable to business interests. Under such a federal mechanism, all participants have the same identity and status, and the federal system helps everyone establish a "common prosperity" strategy. That's why this system is called "federated learning". From a business perspective, the main values of federated learning are:
- Promote cross-domain enterprise-level data cooperation, intelligent strategy to assist market layout and competitiveness improvement: Federated learning, as the underlying technology of AI development, can help enterprises participate in the new globalization, pan-industry collaboration network and federation ecology, Through cross-domain business data cooperation, the model can be trained more effectively to assist its own market layout and strategy optimization, thereby enhancing competitiveness. Federated learning can help enterprises to better establish their own cooperation and competition strategies at the technical level, so as to form a unique ecology in the federation, so as to better promote the healthy development of enterprises.
- A new business model and model based on joint modeling will be born: through the application of federated ecology and federated learning in other fields, it will continue to influence and change the relationship between the provider and the demander in cooperation, and redefine the identities and service methods of all partners and profit methods, giving birth to a new business model and model based on joint modeling.
- Reduce the cost of technology upgrades and promote the development of innovative technologies: The systemic and reusable solutions of federated learning technology can effectively reduce the threshold of technology applications and expand the scope and breadth of technology applications, which enables the majority of pan-AI industry organizations to provide different customers with Richer products and services, and an AI environment that eliminates data security concerns will help to further leap forward innovative technologies, and achieve self-development while improving efficiency and growth.
References:
[1] Yang Qiang, Liu Yang, Cheng Yong, Kang Yan, Chen Tianjian, Yu Han. Federated Learning [M]. Electronic Industry Press, 2020 [2] WeBank, FedAI.
Federated Learning White Paper V2.0. Tencent Research Institute, etc., 2021