Research of Federated Learning Application Methods and Social Responsibility

Abstract

Federated learning is a multi-party distributed machine learning system that allows each participant to complete the training task without their data out of the locality. The real meaning of the realization of data availability is invisible to protect personal privacy and data security. Although some institutions need to obtain their data's benefit by sharing their own data sets, the potential trust risks lead to the data not being better utilized cooperatively. In addition,regulatory requirements on data computing and sharing are different in various countries. These contribute to enormous challenges in the actual application of federated learning. In this research, we make a comprehensive review of the application patterns of federated learning in different fields. Then, we illustrate the social responsibilities of federated learning from four dimensions, including compliance application, system security mechanism, the trust mechanism, and ethical security. Finally, based on the current characteristics and regulatory requirements of federated learning, we discuss the future research directions for federated learning.