EC-MFR: A Hierarchical Edge–Cloud Collaborative Framework for Multimodal Fact-Checking

The spread of multimodal misinformation demands verification that is both accurate and fast while keeping knowledge current. Large language models are powerful but costly and slow, and their static knowledge can lag behind events. We introduce EC-MFR, a hierarchical framework that divides work between edge and the cloud. The system first optionally decomposes the claim into a few targeted sub-claims to guide retrieval, retrieves text and image evidence, and then compresses it into a small set of question–answer items using a lightweight, quantized multimodal language model deployed at the edge. A compact verifier on the edge predicts a label with calibrated confidence. If confidence is high, the decision is returned immediately. If confidence is low, the claim is sent to the cloud where retrieval can be expanded and the reasoning can be redone by a stronger verifier. This design offers three core benefits. It makes reasoning explicit through question–answer items, which shortens prompts and improves auditability. It improves retrieval recall via a light decomposition step that produces targeted sub-queries. Finally, it lets most easy claims finish on the edge to reduce cost and latency while preserving accuracy on difficult claims by allowing the cloud to broaden evidence and refine reasoning. Experiments on MOCHEG and AVERITEC validate the approach. Notably, EC-MFR achieves highly competitive accuracy of 54.10% on the multimodal MOCHEG dataset, and reaches 68.80% on AVERITEC under realistic retrieval settings, outperforming the GPT-4o cloud-only baseline by 6.6 percentage points. Furthermore, system-level profiling on edge hardware demonstrates that EC-MFR reduces processing costs by 51.8% and accelerates inference latency by 2.4× for edge-resolved claims, confirming a highly favorable accuracy–efficiency trade-off compared to existing multimodal fact-checking systems. We also formalize routing and efficiency and analyze calibration and retrieval.