Research Advanced in Federated Learning

Abstract

With the vigorous development of big data, cloud computing and other fields, it has become a global trend to pay attention to data security and privacy. In order to protect their own data security and privacy, different groups are unwilling to contribute their own data information, making the problem of data islands gradually prominent, which seriously restricts the further development of data-driven artificial intelligence. In order to alleviate the above problems, federated learning has attracted more researchers' attention in recent years. Federated Learning is a collaborative decentralized privacy-preserving technique that makes local data available to multiple parties, which not only can the private data be effectively used to train the model but also the leakage of private data can be avoided. Federated learning has been widely used in practical fields such as the financial industry and the Internet of Things industry. This paper systematically introduces the results of research in the field of federated learning in recent years. Specifically, three structures of federated learning are first introduced, and the differences between these three structures are introduced. Then, the most used datasets in training and validation stage were introduced and the shortcoming of each method were introduced to help advanced understanding of FL. Finally, several unsolved problems were introduced and the future prospects in federated learning domain were proposed.