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A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy

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  • Habib Ullah Manzoor

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Attia Shabbir

    (Faculty of Computer Science, Ghulam Ishaq Khan Institute, Topi 23640, Pakistan)

  • Ao Chen

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • David Flynn

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Ahmed Zoha

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

Abstract

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training across multiple devices while preserving data privacy. However, the decentralized nature of FL introduces significant security challenges, making it vulnerable to various attacks targeting models, data, and privacy. This survey provides a comprehensive overview of the defense strategies against these attacks, categorizing them into data and model defenses and privacy attacks. We explore pre-aggregation, in-aggregation, and post-aggregation defenses, highlighting their methodologies and effectiveness. Additionally, the survey delves into advanced techniques such as homomorphic encryption and differential privacy to safeguard sensitive information. The integration of blockchain technology for enhancing security in FL environments is also discussed, along with incentive mechanisms to promote active participation among clients. Through this detailed examination, the survey aims to inform and guide future research in developing robust defense frameworks for FL systems.

Suggested Citation

  • Habib Ullah Manzoor & Attia Shabbir & Ao Chen & David Flynn & Ahmed Zoha, 2024. "A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy," Future Internet, MDPI, vol. 16(10), pages 1-37, October.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:10:p:374-:d:1499237
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    References listed on IDEAS

    as
    1. Rezak Aziz & Soumya Banerjee & Samia Bouzefrane & Thinh Le Vinh, 2023. "Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm," Future Internet, MDPI, vol. 15(9), pages 1-25, September.
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