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Bilateral Pricing for Dynamic Association in Federated Edge Learning

IEEE Transactions on Mobile computing 2025年第6期24卷 4684-4697页

作者： Pan, Bangqi Lu, Jianfeng Cao, Shuqin Liu, Jing Tian, Wenlong Li, Minglu Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Dalian University of Technology Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence China University of South China School of Computer Science and Technology Hengyang421001 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Final: Combining First-Order Logic With Natural Logic for Question Answering

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3103-3117页

作者： Shi, Jihao Ding, Xiao Hui, Siu Cheung Yan, Yuxiong Zhao, Hengwei Liu, Ting Qin, Bing Harbin Institute of Technology Research Center for Social Computing and Information Retrieval Harbin150001 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Many question-answering problems can be approached as textual entailment tasks, where the hypotheses are formed by the question and candidate answers, and the premises are derived from an external knowledge base. However, current neural methods often lack transparency in their decision-making processes. Moreover, first-order logic methods, while systematic, struggle to integrate unstructured external knowledge. To address these limitations, we propose a neuro-symbolic reasoning framework called Final, which combines FIrst-order logic with NAtural Logic for question answering. Our framework utilizes first-order logic to systematically decompose hypotheses and natural logic to construct reasoning paths from premises to hypotheses, employing bidirectional reasoning to establish links along the reasoning path. This approach not only enhances interpretability but also effectively integrates unstructured knowledge. Our experiments on three benchmark datasets, namely QASC, WorldTree, and WikiHop, demonstrate that Final outperforms existing methods in commonsense reasoning and reading comprehension tasks, achieving state-of-the-art results. Additionally, our framework also provides transparent reasoning paths that elucidate the rationale behind the correct decisions. © 2025 IEEE.

关键词： computer circuits

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Average AoI Minimization with Directional Charging for Wireless-Powered Network Edge

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IEEE Transactions on Mobile computing 2025年第6期24卷 4889-4906页

作者： Chen, Quan Guo, Song Xu, Wenchao Li, Jing Shi, Tuo Gao, Hong Cai, Zhipeng Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Hong Kong Polytechnic University Department of Computing Hong Kong999077 Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Zhejiang Normal University School of Computer Science and Technology Jinhua China Georgia State University Department of Computer Science AtlantaGA30303 United States

Age of Information (AoI) has been proposed as a new performance metric to capture the freshness of data. At wireless-powered network edge, the source nodes first need to be charged ready for update transmissions, which means the system AoI is not only decided by the scheduling of update transmissions but also by the designing of charging plan. However, the existing works either only focused on the point of scheduling update transmissions or have a rigid assumption that only one source node can be charged per time. Aiming at making the work more practical and general, we investigate the average AoI optimization problem at wireless-powered network edge with directional charging. Firstly, the theoretical bound of the weighted sum of average AoI of the entire network with a directional charger is analyzed, which is proved to be related to nodes' maximum transmitting interval and the charging strategy. An optimal charging time computation algorithm is proposed to obtain the maximum transmitting interval of each source node by considering the overlapped areas of different charging orientations. After then, an AoI-aware periodical charging scheduling algorithm is proposed to compute a periodical charging schedule while the average AoI is bounded, including a charging period T and the charging orientations assigned to each time slot within T. The proposed algorithm is proved to have an approximation ratio of up to 1.5625. Furthermore, several approximate algorithms are also proposed for average AoI optimization with multiple chargers and network bandwidth constraint. Finally, the extensive simulations demonstrate the high performance of the proposed algorithm in terms of AoI. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Explaining Parkinson's Disease Detection by Ensemble Classifiers with Local Interpretable Features 8th

Explaining Parkinson's Disease Detection by Ensemble Classif...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Karn, Alok Kumar Dixit, Aniket Pati, Peeta Basa Mahalingam, Anand Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

Parkinson's disease (PD) is a neurodegenerative condition and a progressive nervous system disorder condition, marked by its characteristic symptoms such as tremors, muscle stiffness, and slow, imprecise body movements. Typically affecting the middle-aged and elderly, the most crucial challenge in the treatment of PD is posed in the form of its early diagnosis. While there is no cure for the disease, early intervention is found to have alleviated the symptoms and to have also improved the overall quality of life, potentially slowing down the disease’s progression. With the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML), almost every industry seems to be benefiting from its incorporation and the field of biomedical sciences is no different. AI-ML techniques have seemed to be specifically beneficial in classification-based biomedical problems. This research is built upon this very same idea. The application of 14 ML algorithms is studied on a dataset that consists of vocal characteristics of several individuals, both the healthy and the PD patients, to classify based on a patient’s speech whether or not, they have Parkinson’s. To improve the overall performance of the algorithms, SMOTE and PCA pre-processing techniques have also been applied to deal with the dataset’s shortcomings. The study observes that Random Forest, Gaussian Process, Extra Trees, and CatBoost are the 4 best performing algorithms for the task, providing an accuracy of 96.61%. The paper concludes with the deployment of an Explainable AI (XAI) LIME module on the best-performing algorithms to provide transparency and interpretability to the decision-making process of these AI systems. © IFIP International Federation for Information Processing 2025.

关键词： Neurodegenerative diseases

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Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 639-650页

作者： Liu, Kai You, Kang Gao, Pan Paul, Manoranjan Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Charles Sturt University School of Computing and Mathematics BathurstNSW2678 Australia

With the great progress of three-dimensional (3-D) sensing and acquisition technology, the volume of point cloud data has grown dramatically, which urges the development of efficient point cloud compression methods. In this article, we focus on the task of learned lossy point cloud attribute compression (PCAC). We propose an efficient attention-based method for lossy compression of point cloud attributes leveraging on an autoencoder architecture. Specifically, at the encoding side, we conduct multiple downsampling to best exploit the local attribute patterns, in which effective external cross attention (ECA) is devised to hierarchically aggregate features by intergrating attributes and geometry contexts. At the decoding side, the attributes of the point cloud are progressively reconstructed based on the multiscale representation and the zero-padding upsampling tactic. To the best of our knowledge, this is the first approach to introduce attention mechanism to point-based lossy PCAC task. We verify the compression efficiency of our model on various sequences, including human body frames, sparse objects, and large-scale point cloud scenes. Experiments show that our method achieves an average improvement of 1.15 and 2.13 dB in Bjontegaard delta (BD)-peak signal-to-noise ratio (BD-PSNR) of Y channel and YUV channel, respectively, when comparing with the state-of-the-art point-based method deep-PCAC. Codes of this article are available at https://***/I2-Multimedia-Lab/Att2CPC. © 2024 IEEE.

关键词： HTTP

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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AD2T: Adversarial Distortion Domain Translation for Robust Watermarking against Non-differentiable Distortions

AD2T: Adversarial Distortion Domain Translation for Robust W...

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

作者： Zhao, Chengxin Ling, Hefei Chen, Jiazhong Fang, Han Li, Zongyi Xie, Sijing School of Computer Science and Technology Huazhong University of Science and Technology China School of Computing National University of Singapore Singapore

ISBN: (纸本)9798350368741

Deep watermarking models optimize robustness by incorporating distortions between the encoder and decoder. To tackle non-differentiable distortions, current methods only train the decoder with distorted images, which breaks the joint optimization of the encoder-decoder, resulting in suboptimal performance. To address this problem, we propose an Adversarial Distortion Domain Translation (AD2T) method by treating the distortion as an image-to-image translation task. AD2T adopts conditional GANs to learn the non-differentiable distortion mappings. It employs generators to transform the encoded image into the distorted one to bridge the encoder-decoder for joint optimization. We also supervise the GANs to generate challenging distorted samples to augment the watermarking model via adversarial training. This further improves the model robustness by minimizing the maximum decoding loss. Extensive experiments demonstrate the superiority of our method when tested on non-differentiable distortions, including lossy compression and style transfers. Codes are released here: https://***/zcxlanguage/AdversarialDistortionDomainTranslation. © 2025 IEEE.

关键词： adversarial training deep watermarking GANs non-differentiable distortions

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The Past, Present, and Future of Storage Technologies (Part 1 of 2)

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ACM TRANSACTIONS ON STORAGE 2025年第1期21卷 1-3页

作者： Kuenning, Geoff Won, Youjip Zhao, Ming Computer Science Harvey Mudd College Claremont United States Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea (the Republic of) School of Computing and Information Sciences Arizona State University Tempe United States Computer Science Stony Brook University Stony Brook United States

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Cross-Modal Knowledge Diffusion-Based Generation for Difference-Aware Medical VQA

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IEEE Transactions on Image Processing 2025年 34卷 2421-2434页

作者： Lin, Qika He, Kai Zhu, Yifan Xu, Fangzhi Cambria, Erik Feng, Mengling National University of Singapore Saw Swee Hock School of Public Health Queenstown 119077 Singapore Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China Xi’an Jiaotong University School of Computer Science and Technology Shaanxi Xi’an710049 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

Multimodal medical applications have garnered considerable attention due to their potential to offer comprehensive and robust support for medical assistance. Specifically, within this domain, difference-aware medical Visual Question Answering (VQA) has emerged as a topic of increasing interest that enables the recognition of changes in physical conditions over time when compared to previous states and provides customized suggestions accordingly. However, it is challenging because samples usually exhibit characteristics of complexity, diversity, and inherent noise. Besides, there is a need for multimodal knowledge understanding of the medical domain. The difference-aware setting requiring image comparison further intensifies these situations. To this end, we propose a cross-Modal knowlEdge diffusioN-baseD gEneration netwoRk (MENDER), where the diffusion mechanism with multi-step denoising and knowledge injection from global to local level are employed to tackle the aforementioned challenges, respectively. The diffusion process is to gradually generate answers with the sequence input of questions, random noises for the answer masks and virtual vision prompts of images. The strategy of answer nosing and knowledge cascading is specifically tailored for this task and is implemented during forward and reverse diffusion processes. Moreover, the visual and structure knowledge injection are proposed to learn virtual vision prompts to guide the diffusion process, where the former is realized using a pre-trained medical image-text network and the latter is modeled with spatial and semantic graph structures processed by the heterogeneous graph Transformer models. Experiment results demonstrate the effectiveness of MENDER for difference-aware medical VQA. Furthermore, it also exhibits notable performance in the low-resource setting and conventional medical VQA tasks. © 1992-2012 IEEE.

关键词： Biomedical imaging Visualization Medical diagnostic imaging Noise Semantics Medical services Diffusion processes Knowledge engineering Feature extraction Cognition

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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-hop Wireless Networks

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IEEE Transactions on Mobile computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Future Network of Intelligence Institute The School of Science and Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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