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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yixuan Guan Xuefeng Liu Tao Ren Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach.

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Enhancing Resilience of Power Systems with Fuel Cell-Battery Hybrid Energy Storage System

Enhancing Resilience of Power Systems with Fuel Cell-Battery...

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Power science and technology (ICPST), IEEE International Conference on

作者： Mingjian Tuo Muhoza Bertrand Cunzhi Zhao Mulan Zhang Hubei Key Laboratory of Energy Storage and Power Battery Hubei University of Automotive Technology Shiyan China College of Electrical & Information Hubei University of Automotive Technology Shiyan China Department of Engineering and Computer Science McNeese State University Lake Charles Louisiana USA Key Laboratory of Automotive Power Train and Electronics Hubei University of Automotive Technology Shiyan China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

The increasing penetration level of renewable energy resources (RES) has introduced a significant challenge on system frequency dynamics management. Batteries with fast dynamic response are widely used to regulate frequency fluctuation. Considering the degradation of batteries during constant charging and discharging process, it is practical to combine battery with other energy storage to support primary frequency. This paper investigates how hybrid energy storage system including fuel cell (FC) as main and battery as complementary power resources power source can be used to guarantee the frequency stability following the worst contingency while ensuring operational economy. A novel enhanced resilience constrained unit commitment (ER-SCUC) model is proposed to achieve efficient and coordinated utilization of battery energy storage system (BESS) and hydrogen energy storage system (HESS). The economic and low-carbon complementarity between green hydrogen and blue hydrogen are introduced. A case study of a modified IEEE-24 system demonstrates the effectiveness of the proposed ER-SCUC model.

关键词： Fluctuations Power system dynamics Fuel cells Power system stability Green hydrogen Hybrid power systems Stability analysis

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Towards Fair Federated Learning via Unbiased Feature Aggregation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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Towards Task Sampler Learning for Meta-Learning

arXiv

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arXiv 2023年

作者： Wang, Jingyao Qiang, Wenwen Su, Xingzhe Zheng, Changwen Sun, Fuchun Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models;(ii) over-constraining task diversity may incur the risk of under-fitting or overfitting during training;and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://***/WangJingyao07/AdaptiveSampler. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting

arXiv

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arXiv 2025年

作者： Liao, Haicheng Kong, Hanlin Wang, Bonan Wang, Chengyue Ye, Wang He, Zhengbing Xu, Chengzhong Li, Zhenning State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China Senseable City Lab Massachusetts Institute of Technology CambridgeMA United States

Accurate motion forecasting is crucial for safe autonomous driving (AD). This study proposes CoT-Drive, a novel approach that enhances motion forecasting by leveraging large language models (LLMs) and a chain-of-thought (CoT) prompting method. We introduce a teacher-student knowledge distillation strategy to effectively transfer LLMs’ advanced scene understanding capabilities to lightweight language models (LMs), ensuring that CoT-Drive operates in real-time on edge devices while maintaining comprehensive scene understanding and generalization capabilities. By leveraging CoT prompting techniques for LLMs without additional training, CoT-Drive generates semantic annotations that significantly improve the understanding of complex traffic environments, thereby boosting the accuracy and robustness of predictions. Additionally, we present two new scene description datasets, Highway-Text and Urban-Text, designed for fine-tuning lightweight LMs to generate context-specific semantic annotations. Comprehensive evaluations of five real-world datasets demonstrate that CoT-Drive outperforms existing models, highlighting its effectiveness and efficiency in handling complex traffic scenarios. Overall, this study is the first to consider the practical application of LLMs in this field. It pioneers the training and use of a lightweight LLM surrogate for motion forecasting, setting a new benchmark and showcasing the potential of integrating LLMs into AD systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization

DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fiel...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jiahe Li Jiawei Zhang Xiao Bai Jin Zheng Xin Ning Jun Zhou Lin Gu State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Jiangxi Research Institute Beihang University Institute of Semiconductors Chinese Academy of Sciences School of Information and Communication Technology Griffith University RIKEN AIP The University of Tokyo

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views, yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian, a depth-regularized framework based on 3D Gaussian radiance fields, offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting, despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields, we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently, we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping, we introduce Global-Local Depth Normalization, enhancing the focus on small local depth changes. Extensive experiments on LLFF, DTU, and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods, achieving comparable or better results with significantly reduced memory cost, a 25 × reduction in training time, and over 3000 × faster rendering speed. Code is available at: https://***/Fictionarry/DNGaussian.

关键词： Geometry Training Degradation computer vision Three-dimensional displays Costs Color

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A Robust Semantic Communication System for Image Transmission

A Robust Semantic Communication System for Image Transmissio...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiang Peng Zhijin Qin Xiaoming Tao Jianhua Lu Khaled B. Letaief Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China State Key Laboratory of Space Network and Communications Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity.

关键词： Measurement Image communication Perturbation methods Semantics Rician channels Semantic communication Transformers Robustness Streams Signal to noise ratio

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Using Cloud Computing Based Crowdsourcing for Security and Privacy Settings of Android Users

Using Cloud Computing Based Crowdsourcing for Security and P...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yuzhu Sun Xuanyu Liu Tianchi Wu Xiao Fu Bin Luo Xiaojiang Du Mohsen Guizani State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (纸本)9781665435413

With the widespread use of mobile devices, security and privacy concerns have been attracting increasing attention. However, it is challenging for Android users to apply the appropriate settings to protect their devices. Therefore, a cloud-based solution is proposed in this paper, which works based on crowdsourcing to generate recommended security and privacy settings for Android users. Following the suggestions of security experts, ordinary users can readily understand how to properly set up their devices for security enforcement. Our solution collects security-related data from experts, and a weight-based algorithm is introduced to determine data priority and credibility. Finally, the recommended security and privacy settings are created based on these data. The evaluation results demonstrate that our solution can effectively improve the robustness and reliability of Android devices.

关键词： Crowdsourcing Privacy Cloud computing Data privacy Protocols Robustness Mobile security

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An Improvement of Coherent Semantic Attention for Image Inpainting 1

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Weiqing Gu, Enming Fang, Wei School of Computer & Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Jiangsu Key Laboratory of Computer Information Process Technology SooChow University Suzhou China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

In some image restoration algorithms of the past, they often do not consider the continuity between pixels, and the internal features of the hole region. The mapping to the image semantically does not take into account the continuity of the feature, resulting in the color of the fault. Or the deformation of the edge contour of the image. In some of the current popular algorithms and models, we can clearly see the color faults and artificial repair traces from their repair results. These discontinuities are mainly because these methods ignore the semantic relevance and feature continuity of the hole region. Therefore, if we want to get a better image repair effect. We have to improve on semantic relevance and feature continuity. We validated the effectiveness of our proposed method in image restoration tasks on the CelebA and Places2 datasets, and our results yielded a better visual experience in some images than existing methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Research on Microservice Architecture: A Tertiary Study

SSRN

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SSRN 2022年

作者： Liu, Xiaodong Li, Shanshan Zhang, He Zhong, Chenxing Wang, Yanze Waseem, Muhammad Babar, Muhammad Ali State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Institute Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan China Centre for Research on Engineering Software Technologies School of Computer Science University of Adelaide Adelaide Australia Cyber Security Cooperative Research Centre Australia

Context: Microservices architecture (MSA) has gained its popularity for designing software systems. Migrating to MSA is a challenging task and requires comprehensive understanding and careful decisions. Objective: This study aims to characterize secondary studies in the field of MSA from two main points of view, i.e., the topics addressed and their findings, as well as the potential opportunities for the future research. Method: We conducted a tertiary study following the guideline of Kitchenham and collected data from secondary studies of MSA published by the end of 2021. Results: We identified 37 secondary studies in the domain of MSA, including systematic literature reviews and systematic mapping studies that address research questions. The topics addressed, the findings, and the research directions are analyzed in detail in terms of different phases of MSA lifecycle, i.e., Architectural Understanding and Analysis, Architectural Transformation, Architectural Implementation, and Architectural Maintenance and Evolution. Conclusion: Emerging topics require more in-depth research by primary and secondary researchers. Researchers are suggested to have more collaboration with industry and pay more attention to some domains as well as emerging technologies. Tertiary researchers may consider some new types of secondary study with quality control, for example, gray literature review or multivocal literature review. © 2022, The Authors. All rights reserved.

关键词： Quality control

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