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The convergence of AI and IoT has fueled advancements in data science, giving birth to Federated Learning (FL) as a paradigm to preserve data privacy in a decentralized manner. However, practical implementations confront the challenges of inadequate incentives and privacy leakages risks. Hence, this paper focuses on incentive mechanism design within the FL framework, particularly integrating differential privacy, and summarizes the current research literature and indicates future trends through the lenses of economics and game theory. The study firstly overviews the fundamental principles of FL and its advantages in bridging isolated data. It explores how differential privacy, as a reinforced privacy safeguard, can be incorporated into incentive mechanism design, thereby furnishing a robust mathematical foundation for privacy preservation within FL. The equilibrium between user contribution, privacy strength, and economic reward in incentive design are critically analyzed, especially those involving differential privacy-integrated FL mechanisms. Furthermore, it also emphasizes the rationale behind the prevalence of certain game models and explicates the efficacious role of contract theory in incentivizing privacy protection. Through synthesizing analyses, the paper provides in-depth insights for researchers and practitioners in FL domain regarding how to stimulate user engagement through well-calibrated economic and game-theoretic designs while safeguarding privacy, thereby consolidating the foundation for further practical applications and theoretical advancements in FL technology.

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Review of Incentive Mechanisms of Differential Privacy Based Federated Learning Protocols: From the Economics and Game Theoretical Perspectives

  • 会议论文

  • 发表日期：

    2025年

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  • Miaohua Zhuo,
  • Dongjun Li,
  • Qinglin Yang,
  • Yuan Zhou,
  • Yuan Liu,
  • Zhihong Tian

摘要

The convergence of AI and IoT has fueled advancements in data science, giving birth to Federated Learning (FL) as a paradigm to preserve data privacy in a decentralized manner. However, practical implementations confront the challenges of inadequate incentives and privacy leakages risks. Hence, this paper focuses on incentive mechanism design within the FL framework, particularly integrating differential privacy, and summarizes the current research literature and indicates future trends through the lenses of economics and game theory. The study firstly overviews the fundamental principles of FL and its advantages in bridging isolated data. It explores how differential privacy, as a reinforced privacy safeguard, can be incorporated into incentive mechanism design, thereby furnishing a robust mathematical foundation for privacy preservation within FL. The equilibrium between user contribution, privacy strength, and economic reward in incentive design are critically analyzed, especially those involving differential privacy-integrated FL mechanisms. Furthermore, it also emphasizes the rationale behind the prevalence of certain game models and explicates the efficacious role of contract theory in incentivizing privacy protection. Through synthesizing analyses, the paper provides in-depth insights for researchers and practitioners in FL domain regarding how to stimulate user engagement through well-calibrated economic and game-theoretic designs while safeguarding privacy, thereby consolidating the foundation for further practical applications and theoretical advancements in FL technology.