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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

IEEE Internet of Things Journal 2025年第10期12卷 13340-13352页

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guilin University of Electronic Technology Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin541004 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guilin University of Electronic Technology School of Architecture and Transportation Engineering Guilin541004 China Nanning530001 China Beihang University School of Electronic and Information Engineering Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beijing100191 China

Unmanned Aerial Vehicles (UAVs) offer high mobility and flexible deployment capabilities, making them ideal for Internet of Things (IoT) applications. However, the substantial amount of data generated by various applications within the existing low-altitude network requires processing through Deep Neural Networks (DNN) on UAVs, which is challenging due to their limited computational resources. To address this issue, we propose a two-stage optimization method for flight path planning and task allocation based on a mother-child UAV swarm system. In the first stage, we employ a greedy algorithm to solve the path planning problem by considering the task size of the target area to be inspected and the shortest flight path as constraints. The goal is to minimize both the flight path of the UAV and the overall cost of the system. In the second stage, we introduce a novel DNN task assignment algorithm that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Generative Diffusion Models (GDM), named GDM-MADDPG. This algorithm takes advantage of the reverse denoising process of GDM to replace the actor network in MADDPG. It enables UAVs to generate specific DNN task assignment actions based on agents' observations in a dynamic environment, thereby improving the efficiency of task assignment and overall system performance. The simulation results demonstrate that our algorithm outperforms the benchmarks in terms of path planning, Age of Information (AoI), task completion rate, and system utility, demonstrating its effectiveness. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Five-dimensional Floquet topological semimetals with emergent Yang monopoles and linked Weyl surfaces

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Physical Review B 2025年第4期111卷 045302-045302页

作者： Zheng-Rong Liu Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

Recently, Floquet topological matter has attracted significant attention for its potential to reveal novel topological phases inaccessible in static systems. In this paper, we investigate the effect of a time-periodic driving on the five-dimensional (5D) normal insulators. We show that the time-periodic driving can induce a topological phase transition from a TP (time reversal combined with space inversion) symmetry-preserving normal insulator to a 5D Floquet topological semimetal with emergent Yang monopoles characterized by the second Chern number. Additionally, we show that this time-periodic driving can also lead to a topological phase transition from a TP symmetry-breaking normal insulator to a 5D Floquet topological semimetal with a Hopf link formed by Weyl surfaces. Besides, further increasing the strength of the time-periodic driving, the two 5D Floquet topological semimetal phases are transformed into the 5D Floquet Chern insulators. Our paper is helpful for future studies in higher-dimensional Floquet systems.

关键词： Topology

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Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical Networks

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China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment

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LeapGNN: accelerating distributed GNN training leveraging feature-centric model migration 25

LeapGNN: accelerating distributed GNN training leveraging fe...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Xuechen Zhang The State Key Laboratory of Blockchain and Data Security Zhejiang University and Zhejiang Lab and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3.

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An Adaptive DoH Encrypted Tunnel Detection Method Based on Contrastive Learning

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IEEE Internet of Things Journal 2025年

作者： Tong, Jiacheng Zhao, Yilin Jin, Chongju Chen, Wei Zhang, Yiting Wu, Lifa Nanjing University of Posts and Telecommunications School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210003 China

The percentage of encrypted network traffic has constantly increased as network security has been continuously improved. Attackers can, however, utilize encrypted DNS over HTTPS (DoH) to conceal their malicious traffic, which makes it more difficult to identify malicious tunnels. To address this issue, we first examine the encryption features of DoH tunnel traffic. Due to the incapability of current detection techniques to properly fuse traffic attributes, a fusion learning-based method is proposed to detect DoH encrypted tunnel traffic. At the same time, we discover that the DoH traffic samples may exhibit concept drift. As a result, we present a concept drift detection approach based on a contrastive sparse autoencoder. In addition to the above method, a model retraining strategy is also suggested to improve the model’s capacity to identify new DoH encrypted tunnel traffic while reducing its reliance on expert label data. This strategy involves incrementally training the model using as few samples as possible. Experiments demonstrate that the proposed method can significantly enhance detection performance. When 7% of drift samples are used during incremental training, the detection accuracy of the model recovers from 74.02% to 99.96%. © 2014 IEEE.

关键词： Contrastive Learning

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Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Wen, Sian Li, Congyu Chen, Qing Li, Laquan College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China School of Science Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG. © 2025 IEEE.

关键词： federated learning hybrid-supervised learning medical image segmentation

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Disentangled Noisy Correspondence Learning

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IEEE Transactions on Image Processing 2025年 34卷 2602-2615页

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong Xi’an Jiaotong University School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China SGIT AI Laboratory Xi’an710048 China State Grid Corporation of China State Grid Shaanxi Electric Power Company Ltd. Xi’an710048 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc. Beijing100085 China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of modality-invariant information (MII) and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal inputs for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL’s efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. © 1992-2012 IEEE.

关键词： Information theory

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

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ASSD: An AI-Synthesized Speech Detection Scheme Using Whisper Feature and Types Classification

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 542-556页

作者： Chang Liu Xiaolong Xu Fu Xiao Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Current efforts in AI-synthesized speech detection often suffer from decreased performance due to detectors overfitting to irrelevant features or fitting features specific to certain synthesis methods. This issue becomes severe when facing unseen synthesis methods or real-world scenarios. To address these challenges, we first construct a multi-scenario dataset named PolyFake. Subsequently, we propose an AI-Synthesized Speech Detection Scheme using Whisper Feature and Types Classification (ASSD). In the feature processing stage, this method adopts a dual-stream network. Firstly, it utilizes the proposed SC_Encoder to separate and reconstruct redundant features, thereby reducing attention on irrelevant features. Meanwhile, it extracts features through a pre-trained Whisper_Encoder to augment feature generalization ability. Then, features extracted by the two encoders are decomposed, concatenated, and fused in both time and frequency domains, concurrently constructing time and frequency domain graphs. Through heterogeneous stacking graph attention, temporal and spectral information is fused to obtain generalized features. In the detection stage, employing a multi-task learning strategy, we introduce a multi-classification task for predicting synthesis method types to further segregate generalized features, obtaining common features of different synthesis methods for synthesized speech detection. The proposed method achieves lower equal error rate compared to state-of-the-art methods, with a relative improvement of 27% in generalization performance under a 2% increase in parameter count, on the In_the_wild and ASVspoof 2021 DF dataset and the testing scenarios of PolyFake.

关键词： Feature extraction Voice activity detection Training Computational modeling Speech enhancement Multitasking Accuracy Telecommunications Spectrogram security

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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IEEE Symposium on High-Performance Computer Architecture

作者： Siling Yang Shuibing He Wenjiong Wang Yanlong Yin Tong Wu Weijian Chen Xuechen Zhang Xian-He Sun Dan Feng The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Lab Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver Illinois Institute of Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work.

关键词： Training Accuracy Graph convolutional networks Pipelines Energy conservation Computer architecture Machine learning Parallel processing Optimization

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