Collaborative Inference for Large Language Models Against Jamming Attacks

LIN Zhiping; XIAO Liang; CHEN Hongyi; XU Xiaoyu; LI Jieling

doi:10.11999/JEIT250675

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LIN Zhiping, XIAO Liang, CHEN Hongyi, XU Xiaoyu, LI Jieling. Collaborative Inference for Large Language Models Against Jamming Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250675

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LIN Zhiping, XIAO Liang, CHEN Hongyi, XU Xiaoyu, LI Jieling. Collaborative Inference for Large Language Models Against Jamming Attacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250675

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Collaborative Inference for Large Language Models Against Jamming Attacks

doi: 10.11999/JEIT250675 cstr: 32379.14.JEIT250675

School of Infomatics, Xiamen University, Xiamen 361102, China

Funds: The National Natural Science Foundation of China (U21A20444), The National Key Research and Development Program of China (2023YFB3107603)

Received Date: 2025-07-17
Rev Recd Date: 2025-09-10

Available Online: 2025-09-15

Abstract

Abstract

Objective Collaborative inference with Large Language Models (LLMs) is employed to enable mobile devices to offload multi-modal data, including images, text, video, and environmental information such as temperature and humidity, to edge servers. This offloading improves the performance of inference tasks such as human–computer question answering, logical reasoning, and decision support. Jamming attacks, however, increase transmission latency and packet loss, which reduces task completion rates and slows inference. A reinforcement learning–based collaborative inference scheme is proposed to enhance inference speed, accuracy, and task completion under jamming conditions. LLMs with different sparsity levels and quantization precisions are deployed on edge servers to meet heterogeneous inference requirements across tasks. Methods A reinforcement learning–based collaborative inference scheme is proposed to enhance inference accuracy, speed, and task completion under jamming attacks. The scheme jointly selects the edge servers, sparsity rates and quantization levels of LLMs, as well as the transmit power and channels for data offloading, based on task type, data volume, channel gains, and received jamming power. A policy risk function is formulated to quantify the probability of inference task failure given offloading latency and packet loss rate, thereby reducing the likelihood of unsafe policy exploration. Each edge server deploys LLMs with varying sparsity rates and quantization precisions, derived from layer-wise unstructured pruning and model parameter quantization, to process token vectors of multi-modal data including images, text, video, and environmental information such as temperature and humidity. This configuration is designed to meet diverse requirements for inference accuracy and speed across different tasks. The LLM inference system is implemented with mobile devices offloading images and text to edge servers for human–computer question answering and driving decision support. The edge servers employ a vision encoder and tokenizer to transform the received sensing data into token vectors, which serve as inputs to the LLMs. Pruning and parameter quantization are applied to the foundation model LLaVA-1.5–7B, generating nine LLM variants with different sparsity rates and quantization precisions to accommodate heterogeneous inference demands. Results and Discussions Experiments are conducted with three vehicles offloading images (i.e., captured traffic scenes) and texts (i.e., user prompts) using a maximum transmit power of 100 mW on 5,170–5,330 MHz frequency channels. The system is evaluated against a smart jammer that applies Q-learning to block one of the 20 MHz channels within this band. The results show consistent performance gains over benchmark schemes. Faster responses and more accurate driving advice are achieved, enabled by reduced offloading latency and lower packet loss in image transmission, which allow the construction of more complete traffic scenes. Over 20 repeated runs, inference speed is improved by 20.3%, task completion rate by 14.1%, and accuracy by 12.2%. These improvements are attributed to the safe exploration strategy, which prevents performance degradation and satisfies diverse inference requirements across tasks. Conclusions This paper proposed a reinforcement learning–based collaborative inference scheme that jointly selects the edge servers, sparsity rates and quantization levels of LLMs, as well as the transmit power and offloading channels, to counter jamming attacks. The inference system deploys nine LLM variants with different sparsity rates and quantization precisions for human–computer question answering and driving decision support, thereby meeting heterogeneous requirements for accuracy and speed. Experimental results demonstrate that the proposed scheme provides faster responses and more reliable driving advice. Specifically, it improves inference speed by 20.3%, task completion rate by 14.1%, and accuracy by 12.2%, achieved through reduced offloading latency and packet loss compared with benchmark approaches.
- Large Language Model (LLM),
- Multi-modal data,
- Collaborative inference,
- Reinforcement learning,
- Anti-jamming.

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References(28)

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