Work

Synergistic cloud-edge intelligence for real-time multimodal entity linking and knowledge retrieval

Year: 2025

Type: article

Abstract: The synergy between powerful, cloud-hosted large models and efficient, on-device small models presents a new frontier for mobile artificial intelligence. However, enabling real-time, knowledge-intensive interactions, such as querying physical objects in the user's environment, is severely hampered by the high latency of cloud-centric approaches and the limited reasoning capabilities of edge-only models. This paper introduces a novel synergistic cloud-edge intelligence framework designed to bridge this critical gap. Our framework implements a semantic partitioning of labor: a lightweight edge client performs real-time perception to generate compact multimodal features, while an intelligent trigger decides when to offload a structured, minimal-data payload to a powerful cloud-based reasoning engine for definitive entity linking and knowledge-grounded question answering. We validate our approach on SynergyQA, a new benchmark for this task. Experimental results show that our framework reduces end-to-end latency by over 80\% and data transmission by over 98\% compared to naive offloading strategies, while achieving question-answering performance that is competitive with state-of-the-art cloud-only models. Our work provides an efficient and robust blueprint for deploying the next generation of interactive, context-aware AI on mobile and edge devices.

Source: Journal of Cloud Computing Advances Systems and Applications

DOI: 10.1186/s13677-025-00801-5

Authors: Ming Chen, Tianyi Zhang, Ping Qi, Zhihe Chen, Xiaoya Pi

Institution: Tongling University

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,790