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Challenges and future directions of secure federated learning: a survey

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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.

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Acknowledgements

This work was supported by Guangdong Provincial Key Laboratory (2020B121201001).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Kaiyue Zhang

  2. Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, Australia

    Kaiyue Zhang, Chenhan Zhang & Shui Yu

  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

    Xuan Song

  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

    Xuan Song

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  1. Kaiyue Zhang
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  2. Xuan Song
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Correspondence to Xuan Song or Shui Yu.

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Kaiyue Zhang received the BE degree from the Department of Computer Science and Engineering, Southern University of Science and Technology, China in 2019. She is currently pursuing the PhD degree with the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia, and the Department of Computer Science and Engineering, Southern University of Science and Technology. Her research interests include human mobility modeling, urban computing, privacypreserving mechanisms in deep learning.

Xuan Song received the PhD degree from Peking University, China in 2010. In 2017, he was selected as Excellent Young Researcher of Japan MEXT. He led and participated in many important projects as principal investigator or primary actor in Japan, such as DIAS/GRENE Grant of MEXT; Japan/US Big Data and Disaster Project of JST; Young Scientists Grant and Scientific Research Grant of MEXT; Research Grant of MLIT; Grant of JR EAST Company and Hitachi Company. He served as Associate Editor, Guest Editor, Program Chair, Area Chair, Program Committee Member or reviewer for many famous journals and top-tier conferences, such as IMWUT, WWW Journal, ACM TIST, IEEE TKDE, UbiComp, ICCV, CVPR, ICRA.

Chenhan Zhang received the BEng degrees in Telecommunication Engineering from University of Wollongong, Australia, and Zhengzhou University, China in 2017 and 2018, respectively. He received the MS degree in Engineering Management from City University of Hong Kong, China in 2019. He is currently a PhD student at Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. His research interests include deep learning, intelligent transportation systems, privacy-preserving in AI.

Shui Yu obtained his PhD from Deakin University, Australia in 2004. He currently is a Professor of School of Computer Science, University of Technology Sydney, Australia. He has published three monographs and edited two books, more than 400 technical papers, including top journals and conferences, such as IEEE TPDS, TIFS, TMC, TKDE, ToN, and INFOCOM. Dr. Yu initiated the research field of networking for big data, and his research outputs have been widely adopted by industrial systems, such as Amazon cloud security. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials (Area Editor), IEEE Communications Magazine, and so on.

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Zhang, K., Song, X., Zhang, C. et al. Challenges and future directions of secure federated learning: a survey. Front. Comput. Sci. 16, 165817 (2022). https://doi.org/10.1007/s11704-021-0598-z

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