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Related Experiment Videos

A lightweight anonymous authentication scheme for federated learning.

Shu Wu¹, Guoqiang Meng², Linlin Lu³

¹School of Electronic Information and Artificial Intelligence, West Anhui University, Lu'an, 237012, China. wuyshu@126.com.

Scientific Reports

|July 13, 2026

Summary

This study introduces FedLAS, a secure and lightweight anonymous authentication scheme for federated learning. It protects sensitive data and ensures secure interactions in collaborative AI, even in resource-constrained environments.

Keywords:

Distributed data security Federated learning Lightweight anonymous authentication

Related Experiment Videos

Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Federated learning (FL) enables collaborative AI model training without centralizing sensitive user data.
Ensuring privacy and security in FL is critical due to the sensitivity of local data and model parameters.
Mutual authentication is essential to prevent unauthorized access and malicious activities in FL.

Purpose of the Study:

To propose a novel lightweight anonymous authentication scheme for federated learning (FedLAS).
To enhance security and privacy in FL by enabling secure mutual authentication between servers and clients.
To establish a shared session key for encrypted interactions within FL.

Main Methods:

Developed FedLAS, a scheme that avoids computationally expensive cryptographic operations like bilinear pairing.
Implemented mutual authentication protocols to verify identities and secure communication channels.
Conducted informal security analysis to assess resistance against common attacks.

Main Results:

FedLAS successfully achieves secure mutual authentication and establishes session keys.
The scheme demonstrates significantly reduced computational and communication overhead compared to existing methods.
Security analysis confirms FedLAS's ability to meet security requirements and resist attacks.

Conclusions:

FedLAS offers an efficient and secure solution for authentication in federated learning.
Its lightweight nature makes it highly suitable for resource-constrained FL environments.
The proposed scheme enhances the overall security and privacy of collaborative AI systems.