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Related Experiment Videos

Cooperative UAV swarms for zero knowledge verification of edge generative AI using trust-aware multiagent learning.

Kuldashbay Avazov1, Kudratjon Zohirov2, Umidjon Ruziev3

  • 1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.

Scientific Reports
|June 21, 2026
PubMed
Summary

This study introduces a secure framework using unmanned aerial vehicle (UAV) swarms and zero-knowledge proofs to verify generative artificial intelligence (AI) models in edge computing. It enhances reliability and privacy for AI services.

Keywords:
Cooperative UAV swarmsEdge computingGenerative artificial intelligenceModel-as-a-serviceMultiagent reinforcement learningTrust-aware verificationZero-knowledge proofs

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Generative AI integration in edge computing raises concerns about Model-as-a-Service integrity.
  • Edge servers may not adhere to generative models to reduce costs, necessitating validation without compromising proprietary information.

Purpose of the Study:

  • To propose a secure and privacy-preserving framework for verifying edge-based generative AI inference.
  • To address the challenge of validating AI models in edge environments while protecting sensitive data.

Main Methods:

  • A cooperative unmanned aerial vehicle (UAV)-swarm-enabled zero-knowledge verification framework.
  • Edge servers generate interactive cryptographic zero-knowledge proofs for AI execution verification.
  • UAV swarms perform verification operations under mobility and energy constraints.
  • A trust-based multi-agent reinforcement learning approach for UAV swarm behavior planning.

Main Results:

  • The proposed framework significantly improves verification timeliness and reduces malicious server detection delay.
  • Demonstrates enhanced energy efficiency and scalability compared to baseline schemes.
  • The 'age of verification' metric effectively prioritizes risky edge servers.

Conclusions:

  • Integrating cooperative UAV swarms, trust-aware verification, and multi-agent learning provides reliable generative AI services in edge computing.
  • The framework ensures secure, privacy-preserving verification of AI inference at the edge.