Survey on incentive-driven federated learning: privacy and security

CHI Huanhuan; XIONG Ping; LIU Hengzhu; MA Xiao; ZHU Tianqing

doi:10.11959/j.issn.2096-109x.2025035

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Survey on incentive-driven federated learning: privacy and security

Comprehensive Reviews | 更新时间：2025-06-30

- Survey on incentive-driven federated learning: privacy and security
- Chinese Journal of Network and Information Security Vol. 11, Issue 3, Pages: 19-44(2025)
- 作者机构：
  
  1.中南财经政法大学信息工程学院，湖北武汉 430073
  2.澳门城市大学数据科学学院，澳门 999078
- 作者简介：
- 基金信息：
  
  Innovation and Talent Base for Digital Technology and Finance(B21038);The Natural Science Foundation of Hubei Province(2025AFC108;2024AFB957)
- DOI：10.11959/j.issn.2096-109x.2025035
  CLC： TP181
- Received：27 January 2025，
  
  Revised：2025-04-16，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
迟欢欢, 熊平, 刘恒竹, 等. 激励驱动的联邦学习研究综述:隐私与安全[J]. 网络与信息安全学报, 2025, 11(3): 19-44.

Format: CHI H H, XIONG P, LIU H Z, et al. Survey on incentive-driven federated learning: privacy and security[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 19-44.
迟欢欢, 熊平, 刘恒竹, 等. 激励驱动的联邦学习研究综述:隐私与安全[J]. 网络与信息安全学报, 2025, 11(3): 19-44. DOI： 10.11959/j.issn.2096-109x.2025035.

Format: CHI H H, XIONG P, LIU H Z, et al. Survey on incentive-driven federated learning: privacy and security[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 19-44. DOI： 10.11959/j.issn.2096-109x.2025035.

摘要

联邦学习使多个数据持有者能够在不泄露本地数据的前提下协同完成机器学习任务，而鼓励参与者加入联邦学习并提供高质量数据是其成功的关键因素之一。然而，联邦学习在实际应用中面临隐私泄露和安全威胁双重挑战。一方面，恶意参与者通过主动攻击干扰联邦学习的进程和结果，破坏其有效性和鲁棒性；另一方面，被动攻击造成的隐私泄露风险对参与者加入联邦学习的主观意愿造成负面影响，使激励机制的实施变得更加困难。近年来，学术界从隐私保护和安全防御的角度对激励驱动的联邦学习进行了广泛研究，旨在从安全性和公平性兼顾的视角，为联邦学习提供整体解决方案。首先，介绍了激励驱动联邦学习面临的各种被动攻击和主动攻击，深入分析这些攻击引发的隐私泄露风险和安全威胁。然后，分别从隐私保护和安全防御两个方面对激励驱动的联邦学习研究进行综述和分析。在隐私保护方面，着重介绍了差分隐私和同态加密两种技术在激励驱动联邦学习中的应用；在安全防御方面，重点梳理了结合激励机制来防御投毒攻击和搭便车攻击的各种方法。最后，讨论了激励驱动的联邦学习在应对隐私和安全攻击方面面临的挑战，并展望了这一领域未来的研究方向。

Abstract

Federated learning was enabled to allow multiple data holders to jointly complete machine learning tasks without disclosing local data. Incentivizing participants to engage in federated learning and contribute high-quality data was identified as one of the key factors for its success. However

federated learning was found to face challenges in both privacy leakage and security threats in practice. On the one hand

malicious participants were observed to launch active attacks to disrupt the process and results of federated learning

compromising its effectiveness and robustness. On the other hand

privacy leakage risks caused by passive attacks were shown to negatively impact participants’ willingness to join federated learning

making the implementation of incentive mechanisms more difficult. In recent years

extensive research was conducted by the community on incentive-driven federated learning from the perspectives of privacy preservation and security defense

aiming to provide comprehensive solutions that balance security and fairness for federated learning. First

various passive and active attacks faced by incentive-driven federated learning were introduced

and the privacy leakage risks and security threats posed by these attacks were analyzed. Subsequently

a comprehensive review and analysis of incentive-driven federated learning research were provided from the perspectives of privacy preservation and security defense. In terms of privacy preservation

the application of differential privacy and homomorphic encryption technologies in incentive-driven federated learning was focused on. In terms of security defense

various methods that combine incentive mechanisms to defend against poisoning attacks and free-riding attacks were specifically outlined. Finally

the challenges still faced by incentive-driven federated learning in addressing privacy leakage and security attacks were discussed

and potential future research directions in this field were explored.

关键词

Keywords

references

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