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Open Access

ARTICLE

In-Mig: Geographically Dispersed Agentic LLMs for Privacy-Preserving Artificial Intelligence

Mohammad Nauman*
Department of Computer Science, Effat College of Engineering, Effat University, Jeddah, 22332, Saudi Arabia
* Corresponding Author: Mohammad Nauman. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.077259

Received 05 December 2025; Accepted 25 December 2025; Published online 19 January 2026

Abstract

Large Language Models (LLMs) are increasingly utilized for semantic understanding and reasoning, yet their use in sensitive settings is limited by privacy concerns. This paper presents In-Mig, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning in situ, ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis. The architecture features a policy-scoped memory model, utility-driven route planning, and cryptographic trust enforcement. A prototype using JADE for mobility and quantized Mistral-7B demonstrates practical feasibility. Evaluation across various scenarios shows that In-Mig achieves 92% similarity to centralized baselines, confirming its utility and strong privacy guarantees. These results suggest that migrating, privacy-preserving LLM agents can effectively support decentralized reasoning in trust-sensitive domains.

Keywords

Mobile agents; large language models (LLMs); privacy-preserving AI; decentralized reasoning; trust and security

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