arXiv subjects

Jinglong Shen

Publications and source records attributed to Jinglong Shen.

3 recordsLinked to original sources

arXiv2025

A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction

This paper investigates backdoor attacks in image-oriented semantic communications. The threat of backdoor attacks on symbol reconstruction in semantic communication (SemCom) systems has received limited attention. Previous research on backdoor attacks targeting SemCom symbol reconstruction primarily focuses on input-level triggers, which are impractical in scenarios with strict input constraints. In this paper, we propose a novel channel-triggered backdoor attack (CT-BA) framework that exploits inherent wireless channel characteristics as activation triggers. Our key innovation involves utilizing fundamental channel statistics parameters, specifically channel gain with different fading distributions or channel noise with different power, as potential triggers. This approach enhances stealth by eliminating explicit input manipulation, provides flexibility through trigger selection from diverse channel conditions, and enables automatic activation via natural channel variations without adversary intervention. We extensively evaluate CT-BA across four joint source-channel coding (JSCC) communication system architectures and three benchmark datasets. Simulation results demonstrate that our attack achieves near-perfect attack success rate (ASR) while maintaining effective stealth. Finally, we discuss potential defense mechanisms against such attacks.

cs.CR↗

arXiv2023

Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach

As a promising paradigm federated Learning (FL) is widely used in privacy-preserving machine learning, which allows distributed devices to collaboratively train a model while avoiding data transmission among clients. Despite its immense potential, the FL suffers from bottlenecks in training speed due to client heterogeneity, leading to escalated training latency and straggling server aggregation. To deal with this challenge, a novel split federated learning (SFL) framework that pairs clients with different computational resources is proposed, where clients are paired based on computing resources and communication rates among clients, meanwhile the neural network model is split into two parts at the logical level, and each client only computes the part assigned to it by using the SL to achieve forward inference and backward training. Moreover, to effectively deal with the client pairing problem, a heuristic greedy algorithm is proposed by reconstructing the optimization of training latency as a graph edge selection problem. Simulation results show the proposed method can significantly improve the FL training speed and achieve high performance both in independent identical distribution (IID) and Non-IID data distribution.

cs.LG↗

arXiv2023

Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication

Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication. As a result, UAVs have been extensively used as aerial base stations (ABSs) to supplement ground-based cellular networks for various applications. However, existing UAV-assisted communication schemes mainly focus on trajectory optimization and power allocation, while ignoring the issue of collision avoidance during UAV flight. To address this issue, this paper proposes an interpretable UAV-assisted communication scheme that decomposes reliable UAV services into two sub-problems. The first is the constrained UAV coordinates and power allocation problem, which is solved using the Dueling Double DQN (D3QN) method. The second is the constrained UAV collision avoidance and trajectory optimization problem, which is addressed through the Monte Carlo tree search (MCTS) method. This approach ensures both reliable and efficient operation of UAVs. Moreover, we propose a scalable interpretable artificial intelligence (XAI) framework that enables more transparent and reliable system decisions. The proposed scheme's interpretability generates explainable and trustworthy results, making it easier to comprehend, validate, and control UAV-assisted communication solutions. Through extensive experiments, we demonstrate that our proposed algorithm outperforms existing techniques in terms of performance and generalization. The proposed model improves the reliability, efficiency, and safety of UAV-assisted communication systems, making it a promising solution for future UAV-assisted communication applications

eess.SY↗