Abstract: Contributors talk about the application and prospects of Federated Learning and FATE in the data security and privacy protection industry.
Recently, FATE open-source community of WeBank has two new contributors – Yang Liu and Shuqi Qin from Tencent. As experts in the field of cloud computing security, they have constructed a new function point for FATE and fix the bug and then submit to GitHub. From introducing FATE to release incentive mechanisms, more and more practitioners coming from the data security industry involved in the construction of the open-source community. The awareness and the value recognition of FATE have gradually increased and the ecosystem of federated learning is further deepened and expanded.
In the era of AI, data security is a serious problem, Federated Learning is an important method to solve it
The development and popularization of artificial intelligence are constantly changing people’s lives but AI can’t implement without the support of massive data sources. Since big data has become a national strategy, its industry and application develop rapidly. However, in terms of data richness, data quality, data sharing, big data platform security and big data industry ecosystem and so on, the AI industry still has many issues to be solved.
As a kind of distributed machine learning technology based on multiparty secure computation, Federated Learning allows participants to jointly build models without disclosing the underlying data and the encryption (obfuscation) patterns of the underlying data. In industrial applications, it helps different institution break down barriers and conduct AI cooperation. Meanwhile, it can ensure data never leave local storage so that user privacy can be protected. Such a win-win approach of machine learning makes Federated Learning become an essential technology for big data security and privacy protection in the AI era.
FATE (Federated AI Technology Enabler) is the world’s first and industrial level open-source framework of Federated Learning. Launched by WeBank’s AI team,from introducing the GitHub open source to release the contributor incentive mechanisms, WeBank’s AI team expects to embrace all practitioners and build a Federated Learning ecosystem with an open attitude.
FATE strikes directly at the pain point of the industry, builds a bridge for multi-party cooperation in data security
Among the new contributors, Shuqi Qin from Tencent Shield Sandbox mentioned that the industry is facing a lot of pain points. For example, some mobile phone APP exceed users’ authority to obtain user information for profit and derived a large scale of underground industry. There are even underground organizations that decompile popular apps. After modifying the source code, the underground organizations re-release them into the application market by disguising to obtain data and benefits.
Besides, the confirmation right of data is also difficult. Data collected illegally will eventually become commodities in the black market. In the case of data circulation, there is no guarantee that a third party will not secretly copy and abuse the data, even if it is legally authorized to use the data to the third party. The Shield sandbox combined with FATE computing framework can solve the privacy security issue in data flow and provide privacy security solutions for the combination of big data products and AI products. It enables APP manufacturers to reduce the demand for user data and obtain a larger data set with better annotation quality.
Shuqi Qin said that Federated Learning is a new solution to deal with new problems in the new era of big data. In the field of multi-party cooperation in data security, Federated Learning FATE combined with the application of Shield Sandbox can make the data flow more legal and compliance through technical methods to break the “information silos” status.
Develop open-source ecosystem and push data security practitioners to jointly build FATE
It is reported that the core computing module of Tencent Shield Sandbox is provided by FATE. The sandbox project team will actively seek for methods to improve the shortcomings when they use the FATE framework and algorithm and make contributions to FATE open-source project. This form of cooperation also promotes the polishing of Shield Sandbox products and the improvement of FATE project.
With more people joining into the federated ecosystem and push each other to improve their polishing products, this positive cycle will benefit more people. A vibrant open-source ecosystem depends on contributions and the mutual promotion of members.
Currently, the FATE open-source community incentive mechanisms is already online. The contributors participating in the construction will receive official certificates and corresponding incentives. As the world’s first industrial-level open-source community of Federated Learning, FATE has been online on GitHub for only a few months and the Star number has exceeded 700. From the well-known Hong Kong University of Science and Technology, Qinghe Jing, to the well-known technology enterprises Tencent, Yang Liu, Shuqi Qin has participated in the contributions, all of which show that FATE has a strong ability to solve data security. The recent further cooperation between Tencent Cloud Sandbox and FATE shows the reliability and adaptability of FATE. FATE is moving into a broader field.
Federated Learning promotes open cooperation in the field of artificial intelligence and improves the data circulation of the artificial intelligence. Federated Learning also promotes the cooperation among multiple organizations and safe and healthy development of artificial intelligence industry and empowers various application fields. Under this background, whether it is FATE’s open-source or Tencent Cloud’s contribution, we can see the vigorous development of the Federated Learning ecosystem. In the future, more industries will get benefit and the development of China’s data security will also move to a new stage.