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NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory
Haitao Deng1, Jing Hu2, Rohit Sharma3
1Engineering Research Center of Digital Forensics of Ministry of Education, School of Computer, Nanjing University of Information Science and Technology, No. 219, Ningliu Road, Nanjing, Jiangsu, 210044, China.
Computer Communications
|May 4, 2023
Summary
This study introduces NVAS, a novel non-interactive scheme for privacy-preserving federated learning (FL) in COVID-19 detection. NVAS enhances data security and reduces communication burdens, encouraging wider participation in AI-driven health systems.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Game Theory
Background:
- COVID-19 necessitates robust detection and prevention systems, highlighting the role of AI and wireless communication.
- Federated learning (FL) offers a privacy-preserving approach but faces challenges in data security and communication efficiency.
- Existing FL privacy methods are insufficient and increase wireless communication load.
Purpose of the Study:
- To propose a novel non-interactive, verifiable, privacy-preserving FL aggregation scheme (NVAS) for wireless environments.
- To address the limitations of existing FL privacy techniques, specifically insufficient protection and high communication costs.
- To enhance participation and data quality in AI-driven COVID-19 detection systems.
Main Methods:
- Developed NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme.
- Utilized game theory to model FL as a multi-participant game focused on maximizing individual interests.
- Designed an efficient verification algorithm to ensure the accuracy of model aggregation.
Main Results:
- NVAS protects user privacy during FL training without excessive participant interaction.
- The scheme reduces the wireless communication burden compared to existing methods.
- A verification algorithm ensures the integrity of the aggregated model.
Conclusions:
- NVAS offers a secure and efficient solution for privacy-preserving FL in wireless settings.
- The non-interactive nature of NVAS can motivate increased participation and data contribution.
- The proposed scheme is secure, feasible, and enhances AI-driven public health initiatives.
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