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Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure

IEEE Transactions on Dependable and Secure computing 2025年

作者： Deng, Zehang Sun, Ruoxi Xue, Minhui Wen, Sheng Camtepe, Seyit Nepal, Surya Xiang, Yang Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC Australia CSIRO's Data61 Cybersecurity and Quantum Systems Group Australia

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6× to over 100× more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead. © 2004-2012 IEEE.

关键词： Federated learning

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GAEM: Graph-driven Attention-based Entropy Model for LiDAR Point Cloud Compression

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cui, Mingyue Zhong, Yuyang Feng, Mingjian Long, Junhua Ling, Yehua Xu, Jiahao Huang, Kai Sun Yat-sen University School of Computer Science and Engineering Guangzhou400100 China Sun Yat-sen University key Laboratory of Machine Intelligence and Advanced Computing Guangzhou400100 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guangxi Transportation Science and Technology Group Co. Ltd Nanning530029 China

High-quality LiDAR point cloud (LPC) coding is essential for efficiently transmitting and storing the vast amounts of data required for accurate 3D environmental representation. The Octree-based entropy coding framework has emerged as the predominant method, however, previous study usually overly relies on large-scale attention-based context prediction to encode Octree nodes, overlooking the inherent correlational properties of this structure. In this paper, we propose a novel Graph-driven Attention-based Entropy Model (GAEM), which adopts partitioned graph attention mechanisms to uncover contextual dependencies among neighboring nodes. Different from the Cartesian coordinate-based coding mode with higher redundancy, GAEM uses the multi-level spherical Octree to organize point clouds, improving the quality of LPC reconstruction. GAEM combines graph convolution for node feature embedding and grouped-graph attention for exploiting dependency among contexts, which preserves performance in low-computation using localized nodes. Besides, to further increase the receptive field, we design a high-resolution cross-attention module introducing sibling nodes. Experimental results show that our method achieves state-of-the-art performance on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford, compared to all baselines. Compared to the benchmark GPCC, our method achieves gains of up to 53.9% and 53.6% on SemanticKITTI and Ford while compared to the sibling-introduced methods, we achieve up to 42.3% and 44.7% savings in encoding/decoding time. In particular, our GAEM allows for extension to downstream tasks (i.e., vehicle detection and semantic segmentation), further demonstrating the practicality of the method. (Figure presented) © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

引用

Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models.

关键词： Analytical models Load forecasting Biological system modeling Visual analytics Decision making Predictive models data models Planning Forecasting Load modeling

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Rapidvol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans

Rapidvol: Rapid Reconstruction of 3D Ultrasound Volumes from...

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IEEE International Symposium on Biomedical Imaging

作者： Mark C. Eid Pak-Hei Yeung Madeleine K. Wyburd João F. Henriques Ana I.L. Namburete Visual Geometry Group University of Oxford U.K. Oxford Machine Learning in Neuroimaging Lab University of Oxford U.K. College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Two-dimensional (2D) freehand ultrasonography is a widely used medical imaging modality, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to costly 3D ultrasound (US) scanners, 3D volumes can be artificially reconstructed from 2D scans, but this is usually prohibitively slow. Hence, we propose RapidVol: a neural representation framework to speed up slice-to-volume US reconstruction. We use tensor-rank decomposition to decompose the typical 3D volume into tri-planes, which are stored alongside a small neural network. With a set of 2D US scans and their estimated 3D orientation, RapidVol can achieve complete 3D reconstruction. To evaluate our method, we form reconstructions from real fetal brain scans, and then request novel cross-sectional views. Compared to prior fully implicit (e.g. neural radiance field) approaches, our method is over 3x quicker, 46% more accurate, and more robust to errors in pose estimation. We also demonstrate that further speed-up is achievable by reconstructing from a structural prior rather than from random initialisation.

关键词： Three-dimensional displays Accuracy Two-dimensional displays Pose estimation Ultrasonography Neural radiance field Probes Obstetrics Image reconstruction Biomedical imaging

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K-ON: Stacking Knowledge On the Head Layer of Large Language Model

arXiv

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arXiv 2025年

作者： Guo, Lingbing Zhang, Yichi Bo, Zhongpu Chen, Zhuo Sun, Mengshu Zhang, Zhiqiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an entity requires at least several tokens. This leads to a granularity mismatch between KGs and natural languages. To address this issue, we propose K-ON, which integrates KG knowledge into the LLM by employing multiple head layers for next k-step prediction. K-ON can not only generate entity-level results in one step, but also enables contrastive loss against entities, which is the most powerful tool in KG representation learning. Experimental results show that K-ON outperforms state-of-the-art methods that incorporate text and even the other modalities. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge graph

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Early Detection of Breast Cancer with Microcalcifications on Mammography Using Deep Learning

Early Detection of Breast Cancer with Microcalcifications on...

引用

computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ashraf Osman Ibrahim Hafia Mamoun Ismail Abuharaz Mohammed A Saleh Razan Alharith Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Depeartment of Computer Science Al Neelain University Khartoum Sudan DaTA Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia School of Computing and Artificial Intelligence Southwest Jiaotong University

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Breast cancer is among the leading causes of death in women globally, making early detection critical for improving survival rates. Microcalcifications (MCs) on mammographic images are important markers for early detection of breast cancer; yet, identifying and interpreting them can be difficult. Conventional diagnostic procedures sometimes face obstacles due to the complexity and nuance of MC patterns, resulting in higher percentages of missed diagnoses and false positives. This paper develops a deep convolutional neural network (CNN) model to increase the detection and classification accuracy of microcalcifications (MCs) in mammographic images. dataset of 1,093 mammography images used, the proposed model reaches a remarkable training accuracy of 99.98% and testing accuracy of 90.37%. The model's excellent accuracy and low overfitting highlight its potential to assist radiologists in the early detect and diagnose of breast cancer, thereby improving patient outcomes. This study's contribution is the innovative use of advanced deep learning algorithms to a major issue in medical imaging, which represents a significant improvement over current diagnostic approaches.

关键词： Deep learning Training Accuracy Biological system modeling Breast cancer Mammography data models Convolutional neural networks Testing Overfitting

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Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhu, Mingyan Li, Yiming Guo, Junfeng Wei, Tao Xia, Shu-Tao Qin, Zhan Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310007 China University of Maryland Department of Computer Science College ParkMD20742 United States Ant Group Hangzhou310023 China Peng Cheng Laboratory Research Center of Artificial Intelligence Shenzhen518000 China Institute of Blockchain and Data Security Hangzhou310053 China

Currently, sample-specific backdoor attacks (SSBAs) are the most advanced and malicious methods since they can easily circumvent most of the current backdoor defenses. In this paper, we reveal that SSBAs are not sufficiently stealthy due to their poisoned-label nature, where users can discover anomalies if they check the image-label relationship. In particular, we demonstrate that it is ineffective to directly generalize existing SSBAs to their clean-label variants by poisoning samples solely from the target class. We reveal that it is primarily due to two reasons, including (1) the 'antagonistic effects' of ground-truth features and (2) the learning difficulty of sample-specific features. Accordingly, trigger-related features of existing SSBAs cannot be effectively learned under the clean-label setting due to their mild trigger intensity required for ensuring stealthiness. We argue that the intensity constraint of existing SSBAs is mostly because their trigger patterns are 'content-irrelevant' and therefore act as 'noises' for both humans and DNNs. Motivated by this understanding, we propose to exploit content-relevant features, a.k.a. (human-relied) attributes, as the trigger patterns to design clean-label SSBAs. This new attack paradigm is dubbed backdoor attack with attribute trigger (BAAT). Extensive experiments are conducted on benchmark datasets, which verify the effectiveness of our BAAT and its resistance to existing defenses. © 2025 IEEE.

关键词： AI Security Backdoor Attack Clean-label Attack Sample-specific Attack Trustworthy ML

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Squared families: Searching beyond regular probability models

arXiv

引用

arXiv 2025年

作者： Tsuchida, Russell Liu, Jiawei Ong, Cheng Soon Sejdinovic, Dino Data Science and AI Group Monash University ClaytonVIC3800 Australia School of Computing Australian National University CanberraACT2600 Australia Data61 CSIRO Australian National University CanberraACT2600 Australia School of Computer and Mathematical Sciences The University of Adelaide AdelaideSA5005 Australia

We introduce squared families, which are families of probability densities obtained by squaring a linear transformation of a statistic. Squared families are singular, however their singularity can easily be handled so that they form regular models. After handling the singularity, squared families possess many convenient properties. Their Fisher information is a conformal transformation of the Hessian metric induced from a Bregman generator. The Bregman generator is the normalising constant, and yields a statistical divergence on the family. The normalising constant admits a helpful parameter-integral factorisation, meaning that only one parameter-independent integral needs to be computed for all normalising constants in the family, unlike in exponential families. Finally, the squared family kernel is the only integral that needs to be computed for the Fisher information, statistical divergence and normalising constant. We then describe how squared families are special in the broader class of g-families, which are obtained by applying a sufficiently regular function g to a linear transformation of a statistic. After removing special singularities, positively homogeneous families and exponential families are the only g-families for which the Fisher information is a conformal transformation of the Hessian metric, where the generator depends on the parameter only through the normalising constant. Even-order monomial families are the only infinitely differentiable positively homogeneous families, and also admit natural parameter-integral factorisations, unlike exponential families. Finally, we study parameter estimation and density estimation in squared families, in the well-specified and misspecified settings. We use a universal approximation property to show that squared families can learn sufficiently well-behaved target densities at an asymptotic rate of O(N−1/2) + Cn−1/4, where N is the number of datapoints, n is the number of parameters, and C is some data-independ

关键词： Fisher information matrix

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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets

arXiv

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arXiv 2025年

作者： Liu, Hao Guo, Pengyu Yang, Siyuan Jiang, Zeqing Hu, Qinglei Li, Dongyu Hangzhou International Innovation Institute Beihang University Hangzhou311115 China School of Cyber Science and Technology Beihang University Beijing100191 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Ant Group Shanghai200122 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

With the continuous advancement of human exploration into deep space, intelligent perception and high-precision segmentation technology for on-orbit multi-spacecraft targets have become critical factors for ensuring the success of modern space missions. However, the complex deep space environment, diverse imaging conditions, and high variability in spacecraft morphology pose significant challenges to traditional segmentation methods. This paper proposes SpaceSeg, an innovative vision foundation model-based segmentation framework with four core technical innovations: First, the Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) achieves high-precision feature decoding through cross-resolution feature fusion via hierarchical attention. Second, the Multi-spacecraft Connected Component Analysis (MS-CCA) effectively resolves topological structure confusion in dense targets. Third, the Spatial Domain Adaptation Transform framework (SDAT) eliminates cross-domain disparities and resist spatial sensor perturbations through composite enhancement strategies. Finally, a custom Multi-Spacecraft Segmentation Task Loss Function is created to significantly improve segmentation robustness in deep space scenarios. To support algorithm validation, we construct the first multi-scale on-orbit multi-spacecraft semantic segmentation dataset SpaceES, which covers four types of spatial backgrounds and 17 typical spacecraft targets. In testing, SpaceSeg achieves state-of-the-art performance with 89.87% mIoU and 99.98% mAcc, surpassing existing best methods by 5.71 percentage points. The dataset and code are open-sourced at https://***/Akibaru/SpaceSeg to provide critical technical support for next-generation space situational awareness systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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