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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

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Front. Comput. Sci.    2022, Vol. 16 Issue (5) : 165817    https://doi.org/10.1007/s11704-021-0598-z
REVIEW ARTICLE
Challenges and future directions of secure federated learning: a survey
Kaiyue ZHANG1,2, Xuan SONG3,4(), Chenhan ZHANG2, Shui YU2()
1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia
3. SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
4. Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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Abstract

Federated learning came into being with the increasing concern of privacy security, as people’s sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users’ raw data, but aggregates model parameters from each client and therefore protects user’s privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning.
Keywords federated learning      privacy protection      security     
Corresponding Author(s): Xuan SONG,Shui YU   
Just Accepted Date: 12 April 2021   Issue Date: 07 December 2021
 Cite this article:   
Kaiyue ZHANG,Xuan SONG,Chenhan ZHANG, et al. Challenges and future directions of secure federated learning: a survey[J]. Front. Comput. Sci., 2022, 16(5): 165817.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-021-0598-z
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I5/165817
Fig.1  Traditional deep learning versus federated learning. (a) Traditional deep learning. Step A: server collects data from users. Step B: server uses the whole dataset to train the model. Step C: server sends back the complete model to all the users; (b) Federated learning. Step I: server sends the global model to all the users. Step II: each user uses own data to train local model. Step III: each user sends their model to the server. Step IV: server aggregates models as a global model
Fig.2  Five challenges that we mainly discussed in this paper
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