A Game-theoretic Framework for Privacy-preserving Federated Learning
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Abstract (arXiv)
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of textitprivacy leakage cannot be ignored in the presence of textitsemi-honest adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and attackers seems never-ending, we are concerned with one critical question: is it possible to prevent potential attacks in advance? To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks. We name this game the federated learning privacy game (FLPG), in which neither defenders nor attackers are aware of all participants' payoffs. To handle the textitincomplete information inherent in this situation, we propose associating the FLPG with an textitoracle that has two primary responsibilities. First, the oracle provides lower and upper bounds of the payoffs for the players. Second, the oracle acts as a correlation device, privately providing suggested actions to each player. With this novel framework, we analyze the optimal strategies of defenders and attackers. Furthermore, we derive and demonstrate conditions under which the attacker, as a rational decision-maker, should always follow the oracle's suggestion textitnot to attack.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 56 references that are in the map. First posted on arXiv 11 Apr 2023.
Builds on
22 of 56 references.
References · 22
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- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2016Deep Learning with Differential Privacycited by 2,728
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2019Deep Leakage from Gradientscited by 1,021
- 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
- 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
- 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
- 2020iDLG: Improved Deep Leakage from Gradientscited by 382
- 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
- 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
10 more
- 2018Distributed learning of deep neural network over multiple agentscited by 279
- 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
- 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 132
- 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
- 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
- 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
- 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
- 2023From Robustness to Privacy and Backcited by 27
- 2021Federated Deep Learning with Bayesian Privacycited by 13
- 2018Nash Equilibria in the Response Strategy of Correlated Gamescited by 1
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2 papers.