Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)

  • 2023.05a
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  • Pages.230-232
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  • 2023
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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A Survey on Threats to Federated Learning

연합학습의 보안 취약점에 대한 연구동향

  • Woorim Han (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center(ISRC), Seoul National University) ;
  • Yungi Cho (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center(ISRC), Seoul National University) ;
  • Yunheung Paek (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center(ISRC), Seoul National University)
  • 한우림 (서울대학교 전기정보공학부, 서울대학교 반도체 공동연구소) ;
  • 조윤기 (서울대학교 전기정보공학부, 서울대학교 반도체 공동연구소) ;
  • 백윤흥 (서울대학교 전기정보공학부, 서울대학교 반도체 공동연구소)
  • Published : 2023.05.18
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Abstract

Federated Learning (FL) is a technique that excels in training a global model using numerous clients while only sharing the parameters of their local models, which were trained on their private training datasets. As a result, clients can obtain a high-performing deep learning (DL) model without having to disclose their private data. This setup is based on the understanding that all clients share the common goal of developing a global model with high accuracy. However, recent studies indicate that the security of gradient sharing may not be as reliable as previously thought. This paper introduces the latest research on various attacks that threaten the privacy of federated learning.

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Acknowledgement

This work was supported by the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2023. Also, this research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2023-2020-0-01602) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation). This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2020-0-01840,Analysis on technique of accessing and acquiring user data in smartphone).