A Data Protection-Oriented Design Procedure for a Federated Learning Framework

Abstract

Artificial intelligence is a bellwether of today’s new technological revolution. As a branch of artificial intelligence, machine learning faces two main problems in practical application: 1) data owned by most enterprises are difficult to aggregate; and, 2) big data owners pay more and more attention to data privacy and security, which leads to the problem of data island. Federated learning (FL), as a distributed FL paradigm, which can enable all parties to achieve the purpose of co-building models while ensuring data privacy and exposure, provides a possible solution to the mentioned problems of machine learning. The FL takes full advantage of participants’ data and computing power and builds a more robust machine learning model without sharing the data. In an environment with strict data regulation, the FL can effectively solve the key problems, such as those related to data privacy and data rights. However, most of the existing FL-based frameworks do not pay attention to the impact of data source distribution on FL training. Therefore, this paper proposed a data-oriented FL framework called the Federated AI Engine(FAE), which can solve FL problems without leaving the data in control. The proposed framework provides a method that can be used to verify FL quickly for researchers who intend to try federal learning.