A Game-theoretic Framework for Privacy-preserving Federated Learning Analysis

Grunnleggende konsepter

The authors propose a game-theoretic framework to address privacy concerns in federated learning, considering both defenders and attackers' payoffs. They introduce an oracle to provide lower and upper bounds of payoffs, facilitating the analysis of optimal strategies.

Sammendrag

The content discusses a novel game-theoretic framework for privacy-preserving federated learning. It introduces the concept of FLPG, where defenders aim to optimize model utility while minimizing privacy leakage risks. The oracle provides guidance by suggesting actions based on correlation probabilities. The authors analyze conditions for robust equilibrium and derive strategies for defenders and attackers.

Key points include:

• Introduction of FLPG for collaborative optimization in federated learning.
• Consideration of protection mechanisms and attacking strategies.
• Proposal of an oracle to provide payoffs estimation and suggested actions.
• Analysis of optimal strategies for defenders and attackers in FLPG.

Statistikk

In federated learning, benign participants aim to optimize a global model collaboratively.
Proposed game-theoretic framework considers computational costs, model utilities, and privacy leakage risks.
Oracle provides lower and upper bounds of payoffs for players in FLPG.

Sitater

"The primary challenge is the absence of a suitable method for assessing the benefits of the defender."
"Privacy leakage presents a conflicting goal between defenders and attackers."

Viktige innsikter hentet fra

A Game-theoretic Framework for Privacy-preserving Federated Learning

by Xiaojin Zhan... klokken arxiv.org 02-29-2024

https://arxiv.org/pdf/2304.05836.pdf

Dypere Spørsmål

How can the proposed game-theoretic framework be applied in real-world scenarios

The proposed game-theoretic framework for privacy-preserving federated learning can be applied in real-world scenarios by providing a structured approach to analyzing the interactions between defenders (clients) and attackers (server) in a federated learning setting. By considering factors such as model utility, privacy leakage, protection costs, and attacking costs, the framework offers a comprehensive way to evaluate strategies and outcomes in collaborative machine learning environments.
In practical applications, this framework could be utilized by organizations implementing federated learning protocols to assess the trade-offs between model performance improvements and potential privacy risks. By incorporating insights from game theory, decision-makers can make informed choices about protection mechanisms, resource allocation for defense strategies, and risk mitigation tactics against semi-honest adversaries.

What are potential limitations or drawbacks of relying on an oracle for decision-making

While utilizing an oracle for decision-making in the context of FLPG provides valuable guidance on payoffs and strategy selection based on estimated bounds, there are potential limitations that should be considered:

Dependency on Accuracy: The effectiveness of decisions made based on oracle suggestions relies heavily on the accuracy of the estimated payoffs provided. Inaccurate estimations could lead to suboptimal strategies or outcomes.

Lack of Adaptability: Oracles may not adapt well to dynamic or evolving situations where new information or threats emerge rapidly. This rigidity could limit their effectiveness in responding to changing circumstances.

Overreliance Risk: Depending too much on oracle recommendations without critical analysis or human judgment may lead to complacency or lack of creativity in devising innovative solutions.

Resource Intensive: Implementing an oracle system requires resources for data collection, analysis algorithms development, maintenance costs which might not always be feasible for all organizations.

Privacy Concerns: The use of an oracle introduces another layer of complexity regarding data privacy since it involves processing sensitive information related to payoffs and strategies.

How might advancements in encryption technologies impact the effectiveness of protection mechanisms in FLPG

Advancements in encryption technologies have the potential to significantly impact the effectiveness of protection mechanisms within FLPG:

Enhanced Data Security: Improved encryption techniques like homomorphic encryption can provide stronger safeguards for private data during communication between clients and servers.

Reduced Privacy Risks: Advanced cryptographic methods can minimize exposure risks associated with gradient-based attacks by ensuring that even intermediate computations remain encrypted.

Increased Complexity: While stronger encryption enhances security levels, it also adds computational overhead due to increased complexity in encrypting/decrypting data which might impact overall efficiency.

4 .Interoperability Challenges: Compatibility issues may arise when integrating cutting-edge encryption technologies into existing federated learning systems leading potentially disruptive transitions.
5 .Regulatory Compliance: Organizations must ensure compliance with relevant regulations concerning data protection when implementing advanced encryption measures within FLPG frameworks