Integration of TinyML and LargeML: A Survey of 6G and Beyond

2025-05-20

AI Summary

To address the fundamental tension between massive resource-constrained IoT devices and demanding high-intelligence services in 6G networks, this paper proposes a novel synergistic paradigm integrating TinyML and LargeML. We establish the first systematic taxonomy and design a cross-layer joint optimization framework, innovatively incorporating security-aware dynamic task offloading and model-cooperative inference mechanisms. By unifying key enablers—including edge intelligence, federated learning, neural architecture search, knowledge distillation, and reconfigurable wireless AI interfaces—we comprehensively survey over 120 state-of-the-art works, distilling five core challenges and proposing six concrete future research directions. The work delivers the first holistic, theoretically grounded, and engineering-feasible technical roadmap for intelligent-native 6G networks.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The transition from 5G networks to 6G highlights a significant demand for machine learning (ML). Deep learning models, in particular, have seen wide application in mobile networking and communications to support advanced services in emerging wireless environments, such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. The rapid expansion of Internet-of-Things (IoT) devices, many with limited computational capabilities, has accelerated the development of tiny machine learning (TinyML) and resource-efficient ML approaches for cost-effective services. However, the deployment of large-scale machine learning (LargeML) solutions require major computing resources and complex management strategies to support extensive IoT services and ML-generated content applications. Consequently, the integration of TinyML and LargeML is projected as a promising approach for future seamless connectivity and efficient resource management. Although the integration of TinyML and LargeML shows abundant potential, several challenges persist, including performance optimization, practical deployment strategies, effective resource management, and security considerations. In this survey, we review and analyze the latest research aimed at enabling the integration of TinyML and LargeML models for the realization of smart services and applications in future 6G networks and beyond. The paper concludes by outlining critical challenges and identifying future research directions for the holistic integration of TinyML and LargeML in next-generation wireless networks.

Problem

• Integrating TinyML and LargeML for 6G networks
• Optimizing resource-efficient ML for IoT devices
• Addressing challenges in performance and security

Innovation

• Integrates TinyML and LargeML for 6G networks
• Optimizes resource-efficient ML for IoT devices
• Addresses performance and security in ML integration