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Robust federated contrastive recommender system against targeted model poisoning attack

Science China(Information Sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Two-Stage Planning for Smart Buildings With Flexible Heating Load Considering Climate Change Induced Heat Waves

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IEEE Transactions on Smart Grid 2025年第3期16卷 2012-2025页

作者： Zhao, Tianyang Xu, Qianwen KTH Royal Institute of Technology School of Electrical Engineering and Computing Sciences Stockholm114 58 Sweden

Building energy planning is significantly challenged by climate change, particularly the increasing frequency of heat waves impacting heating and cooling demands. Current planning methodologies neglect the impacts of heat waves on energy consumption and do not accurately model the temperature-dependent performance of heat pumps (HPs). This paper addresses the critical issue of designing energy-efficient and climate-resilient buildings through optimal resource configuration under uncertain weather conditions. A two-stage stochastic optimization model for building energy system planning is proposed. In the first stage, the capacities of energy resources are optimized;in the second stage, operational strategies under various weather scenarios are determined. A novel long-term load forecasting method using morphing techniques is developed to generate scenario trees accounting for both normal conditions and heat waves, capturing the impact of climate change on energy demand. Additionally, a temperature-dependent HP model with finite partial output levels is introduced, improving upon existing fixed coefficient of performance models to reflect practical operational characteristics. Simulation results on a real educational building in Stockholm demonstrate the effectiveness of the approach, showing an 8.33% reduction in heating capacity requirements and a 62.14% decrease in solution time, enhancing both resilience and computational efficiency. © 2010-2012 IEEE.

关键词： Stochastic systems

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CRIS-b: A High-Speed Unified Modulo Reduction Algorithm and Hardware Architecture for CRYSTALS-Kyber 38

CRIS-b: A High-Speed Unified Modulo Reduction Algorithm and ...

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38th International Conference on VLSI Design, VLSID 2025

作者： Majumdar, Alip Shrestha, Rahul School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9798331522445

This paper presents highly reliable algorithm and high-speed hardware architecture for a unified modulo reduction for CRYSTALS-Kyber. This new architecture for modulo reduction is capable of operating at a maximum clock frequency of 351.62 MHz on Artix-7 FPGA platform, consuming a latency of 5 clock cycles, which is approximately 35.24% faster than state-of-the-art implementations. In this work, we have alleviated the size expansion problem of intermediate value of the conventional Barrett algorithm (which is one of the fastest and most reliable algorithm for modulo reduction, reported in literature) by suggesting a new technique, referred as 'Conditional R-index Switching for b-value Minimization (CRIS-b)'. We propose a unified modulo reduction design handling both positive and negative spectrum numbers for all arithmetic modulo operations in a butterfly unit of Number Theoretic Transform(NTT). © 2025 IEEE.

关键词： Quantum cryptography

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Joint Distributed Estimation and Random Gain Channel Estimation with Beta Prior

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IEEE Communications Letters 2025年第5期29卷 1008-1012页

作者： Zayyani, Hadi Korki, Mehdi Torkamani, Razieh Department of Electrical and Computer Engineering Qom Iran Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne Australia Bu-Ali Sina University Faculty of Engineering Department of Electrical Engineering Hamedan Iran

In this letter, we propose a joint distributed estimation and channel estimation algorithm for wireless sensor networks (WSNs). We assume a random gain channel model with a Beta prior, where the channel gain is an attenuation factor ranging between zero and one. To estimate the augmented unknown vector, which includes both the unknown vector and the channel gain vector, we employ Maximum A Posteriori (MAP) estimation. This is achieved through an iterative steepest-descent method to find the MAP estimators for both the unknown vector and the channel gain vector. We mathematically derive the optimum combination coefficients that minimize disturbance. Additionally, we propose a separate approach for channel gain estimation using Least Squares (LS) and provide the convexity analysis of the cost function along with the sufficient conditions for the convergence of the iterative channel gain estimator. Simulation results demonstrate the effectiveness of the proposed algorithm compared to some other algorithms in the literature, specially when channel gains are modeled with a Beta prior. © 1997-2012 IEEE.

关键词： Cost functions

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Context-Aware Selection of Machine Learning as a Service (MLaaS) in IoT Environments

Context-Aware Selection of Machine Learning as a Service ...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st International Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th International Conference on Web Information Systems engineering, WISE 2024

作者： Patel, Keya Mistry, Sajib Kanneganti, Deepak Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9789819614820

The integration of Machine Learning as a Service (MLaaS) into the Internet of Things (IoT) environments presents considerable opportunities for enhancing decision-making and automation. We propose a novel framework for context-aware selection of MLaaS in IoT settings, aimed at optimising the interaction between IoT users’ activities and machine learning services. Our framework considers various contextual dimensions, such as user preferences, locations, IoT device capabilities, and application requirements, to develop a dynamic selection process. By employing context-aware algorithms, our approach seeks to enhance the efficiency, accuracy, and responsiveness of IoT systems. We propose a context change analysis algorithm based on support vector machines (SVM). We develop a contextual bandits algorithm along with skyline queries to achieve optimal mapping between abstract MLaaS services and concrete MLaaS services for quality of service (QoS) attributes. Experiments conducted with real-world and simulated datasets demonstrate the effectiveness of our proposed methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Some Considerations for the Preservation of Endangered Languages Using Low-Resource Machine Translation 37th

Some Considerations for the Preservation of Endangered La...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Kho, Alastair Pham, Duc-Son Soon, Susannah Chan, Kit Yan School of Electrical Engineering Computing and Mathematical Sciences Curtin University BentleyWA Australia

ISBN: (纸本)9789819603473

While widespread languages remain actively prevalent in digital mediums, endangered languages such as Indigenous Australian languages, are often scarce in textual resources and lack a substantial digital presence. This diminishes the survivability of their language and cultural heritages, making language preservation initiatives important. Neural Machine Translation (NMT) efforts for low-resource languages can help accelerate the digitisation and preservation of such languages by further enabling translation, information access, and second-language acquisition. This study explores the challenges and considerations with low-resource machine translation (MT), specifically focusing on primarily oral Indigenous Australian languages with minimally available textual resources. Additionally, we explore the existing challenges in the search for quality data and ethical research considerations in approaching Indigenous Cultural Intellectual Property (ICIP). As NMT performance often scales with the quality and quantity of multilingual corpora, we explore promising alternatives such as leveraging large language models (LLMs) to tackle severely low-resource MT as a few-shot prompting translation task. By employing a data imputation approach inspired by Continuous Bag-of-Words (CBOW) to strengthen a prompt’s contextual relevancy, we enhance translations generated by LLMs, achieving a chrF score of 37.3 on imputed data, compared to a baseline of 31.6 with GPT-3.5, and 39.3 compared to a baseline of 38.3 on GPT-4. Through our work, we hope to establish a foundation for future efforts in preserving Indigenous Australian languages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer aided language translation

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meMIA: Multilevel Ensemble Membership Inference Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 93-106页

作者： Ullah, Najeeb Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing LincolnNE68588 United States

Leakage of private information in machine learning models can lead to breaches of confidentiality, identity theft, and unauthorized access to personal data. Ensuring the safe and trustworthy deployment of AI systems necessitates addressing privacy concerns to prevent unintentional disclosure and discrimination. One significant threat, membership inference (MI) attacks, exploit vulnerabilities in target learning models to determine if a given sample was part of the training set. However, the effectiveness of existing MI attacks is often limited by the number of classes in the dataset or the need for diverse multilevel adversarial features to exploit overfitted models. To enhance MI attack performance, we propose meMIA, a novel framework based on stacked ensemble learning. meMIA integrates embeddings from a neural network (NN) and a long short-term memory (LSTM) model, training a subsequent NN, termed the meta-model, on the concatenated embeddings. This method leverages the complementary strengths of NN and LSTM models;the LSTM captures order differences in confidence scores, while the NN discerns probability distribution differences between member and nonmember samples. We extensively evaluate meMIA on seven benchmark datasets, demonstrating that it surpasses current state-of-the-art MI attacks, achieving accuracy up to 94.6% and near-perfect recall. meMIA's superior performance, especially on datasets with fewer classes, underscores the urgent need for robust defenses against privacy attacks in machine learning, contributing to the safer and more ethical use of AI technologies. © 2024 IEEE.

关键词： Information leakage

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Robust facial expression recognition via lightweight reinforcement learning for rehabilitation robotics

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Optoelectronics Letters 2025年第2期21卷 97-104页

作者： CHEN Yifan FAN Weiming GAO Hongwei YU Jiahui JU Zhaojie School of Computing University of PortsmouthPO13HEUK School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Biomedical Engineering Zhejiang UniversityHangzhou 310027China

This paper proposes a lightweight reinforcement network (LRN) and auxiliary label distribution learning (ALDL)based robust facial expression recognition (FER) *** designed representation reinforcement (RR) network mainly comprises two modules,i.e.,the RR module and the auxiliary label space construction (ALSC) *** RR module highlights key feature messaging nodes in feature maps,and ALSC allows multiple labels with different intensities to be linked to one ***,LRN has a more robust feature extraction capability when model parameters are greatly reduced,and ALDL is proposed to contribute to the training effect of LRN in the condition of ambiguous training *** tested our method on FER-Plus and RAF-DB datasets,and the experiment demonstrates the feasibility of our method in practice during rehabilitation robots.

关键词： rehabilitation lightweight auxiliary

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Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Biomedical Engineering Zhejiang UniversityHangzhou 310058China School of Computing University of PortsmouthPO13HEUK

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases,with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC).Although deep learning holds promise in mitigating missed polyp rates,modern endoscopy examinations pose additional challenges,such as image blurring and *** study explores lightweight yet powerful attention mechanisms,introducing the spatial-channel transformer(SCT),an innovative approach that leverages spatial channel relationships for attention weight *** method utilizes rotation operations for inter-dimensional dependencies,followed by residual transformation,encoding inter-channel and spatial information with minimal computational *** experiments on the CVC-Clinic DB polyp detection dataset,addressing endoscopy pitfalls,underscore the superiority of our SCT over other state-of-the-art *** proposed model maintains high performance,even in challenging scenarios.

关键词： diagnosis utilize holds

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Spatiotemporal Pre-Trained Large Language Model for Forecasting with Missing Values

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IEEE Internet of Things Journal 2025年第10期12卷 13838-13850页

作者： Fang, Le Xiang, Wei Pan, Shirui Salim, Flora D. Chen, Yi-Ping Phoebe Tianjin University School of Electrical and Information Engineering Tianjin China La Trobe University School of Computing Engineering and Mathematical Sciences Melbourne3086 Australia James Cook University College of Science and Engineering CairnsQLD4870 Australia Griffith University School of Information and Communication Technology BrisbaneQLD Australia UNSW University School of Computer Science and Engineering Sydney Australia La Trobe University School of Computing Engineering and Mathematical Sciences MelbourneVIC3086 Australia

Spatiotemporal data collected by sensors within an urban Internet of Things (IoT) system inevitably contains some missing values, which significantly affects the accuracy of spatiotemporal data forecasting. However, existing techniques, including those based on Large Language Models (LLMs), show limited effectiveness in forecasting with missing values, especially in scenarios involving high-dimensional sensor data. In this article, we propose a novel spatiotemporal pre-trained large language model dubbed SPLLM for forecasting with missing values. In this network, we seamlessly integrate a specialized spatiotemporal fusion Graph Convolutional Network (GCN) module that extracts intricate spatiotemporal and graph-based information, for generating suitable inputs to the SPLLM. Furthermore, we propose a Feed-Forward Network (FFN) fine-tuning strategy within the LLM and a final fusion layer to enable the model to leverage the pre-trained foundational knowledge of the LLM and adapt to new incomplete data simultaneously. The experimental results indicate that SPLLM outperforms state-of-the-art models on real-world public datasets. Notably, SPLLM exhibits a superior performance in tackling incomplete sensory data with a variety of missing rates. A comprehensive ablation study of key components is conducted to demonstrate their efficiency. © 2014 IEEE.

关键词： Forecasting Spatiotemporal phenomena Predictive models Sensors Data models Time series analysis Adaptation models Internet of Things Large language models Accuracy

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