[2603.02579] Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems

Computer Science > Machine Learning arXiv:2603.02579 (cs) [Submitted on 3 Mar 2026] Title:Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems Authors:Mengru Wu, Jiawei Li, Jiaqi Wei, Bin Lyu, Kai-Kit Wong, Hyundong Shin View a PDF of the paper titled Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems, by Mengru Wu and 5 other authors View PDF HTML (experimental) Abstract:With the increasing computational demands of deep neural network (DNN) inference on resource-constrained devices, DNN partitioning-based device-edge collaborative inference has emerged as a promising paradigm. However, the transmission of intermediate feature data is vulnerable to malicious jamming, which significantly degrades the overall inference performance. To counter this threat, this letter focuses on an anti-jamming collaborative inference system in the presence of a malicious jammer. In this system, a DNN model is partitioned into two distinct segments, which are executed by wireless devices and edge servers, respectively. We first analyze the effects of jamming and DNN partitioning on inference accuracy via data regression. Based on this, our objective is to maximize the system's revenue of delay and accuracy (RDA) under inference accuracy and computing resource constraints by jointly optimizing computation resource allocation, devices' transmit power, and DNN partitioning....