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arXiv 2025年

作者： Chen, Xi Li, Qin Cai, Haibin Wang, Ting MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China

Federated Learning (FL) empowers multiple clients to collaboratively train machine learning models without sharing local data, making it highly applicable in heterogeneous Internet of Things (IoT) environments. However, intrinsic heterogeneity in clients’ model architectures and computing capabilities often results in model accuracy loss and the intractable straggler problem, which significantly impairs training effectiveness. To tackle these challenges, this paper proposes a novel Heterogeneity-aware Personalized Federated Learning method, named HAPFL, via multi-level Reinforcement Learning (RL) mechanisms. HAPFL optimizes the training process by incorporating three strategic components: 1) An RL-based heterogeneous model allocation mechanism. The parameter server employs a Proximal Policy Optimization (PPO)-based RL agent to adaptively allocate appropriately sized, differentiated models to clients based on their performance, effectively mitigating performance disparities. 2) An RL-based training intensity adjustment scheme. The parameter server leverages another PPO-based RL agent to dynamically fine-tune the training intensity for each client to further enhance training efficiency and reduce straggling latency. 3) A knowledge distillation-based mutual learning mechanism. Each client deploys both a heterogeneous local model and a homogeneous lightweight model named LiteModel, where these models undergo mutual learning through knowledge distillation. This uniform LiteModel plays a pivotal role in aggregating and sharing global knowledge, significantly enhancing the effectiveness of personalized local training. Experimental results across multiple benchmark datasets demonstrate that HAPFL not only achieves high accuracy but also substantially reduces the overall training time by 20.9%-40.4% and decreases straggling latency by 19.0%-48.0% compared to existing solutions. © 2025, CC BY.

关键词： Federated learning

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A Range/Angle-based Localization Framework in WSN: A Clustering Approach

A Range/Angle-based Localization Framework in WSN: A Cluster...

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Sensing, Measurement, communication and Internet of Things Technologies (SMC-IoT), International conference on

作者： Sijie Kong Bo Xiao MOE Research Center for Software/Hardware Co-Design Engineering and Application Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9798350373257

ISBN: (纸本)9798350373264

In this paper, we propose a localization algorithm framework for wireless sensor networks (WSNs) based on cluster implementation. The algorithm addresses the issue of information locality in traditional distributed localization algorithms by utilizing clustering. In contrast to centralized algorithms, this algorithm achieves a more balanced node energy consumption without the need for additional special nodes. Through clustering, nodes pass appropriate information to their targets, and through optimizations within and between clusters, relatively accurate location information is obtained. After multiple rounds of clustering-localization iterations, the accuracy of node localization significantly improves. Importantly, simulation results demonstrate that the algorithm can adapt to various deployment scenarios by adjusting clustering parameters to balance energy consumption and localization accuracy.

关键词： Location awareness Wireless sensor networks Energy consumption Simulation Clustering algorithms Sensors Internet of Things

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Flipping consistent and counterfactual Attention Network for Facial Expression Recognition

Flipping Consistent and Counterfactual Attention Network for...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenjie Liu Xinlong Shi Xianzhong Liu School of Software Engineering East China Normal University Shanghai China Software/Hardware Co-Design Engineering Research Center MoE

Facial expression recognition (FER) has recently made great process with attention mechanisms. However, existing methods mostly treat the network as a black box and ignore the causal relationship between modules. In this paper, we propose a flipping consistent and counterfactual attention network (FCCAN), which innovatively uses two causal inferences to guide the learning process. First, we construct an attention network with a multi-head attention learning (MAL) module to learn attention for different local features. Then, we measure and improve the quality of each component with causal reasoning. According to the consistency of features from flipped images, we design flipping consistency learning (FCL) module to force the network to extract comprehensive expression features. Based on counterfactual causality, we propose counterfactual attention learning (CAL) module to encourage the network to learn more effective attention. Extensive experiments implemented on FER datasets (including RAF-DB, FERPlus and AffectNet) show that our FCCAN outperforms state-of-the-art methods.

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Sparrow: Flexible Memory Deduplication in Android Systems with Similar-Page Awareness

Sparrow: Flexible Memory Deduplication in Android Systems wi...

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design, Automation and Test in Europe conference and Exhibition

作者： Guangyu Wei Changlong Li Rui Xu Qingfeng Zhuge Edwin H.-M. Sha School of Computer Science and Technology East China Normal University MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application Department of Computer Science City University of Hong Kong

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Mobile devices have become ubiquitous in daily life. In contrast to traditional servers, mobile devices suffer from limited memory resources, leading to a significant degradation in the user experience. This paper demonstrates that the primary cause of memory consumption lies in anonymous pages associated with application heaps. Existing schemes are ineffective in deduplicating these pages due to the limited occurrence of the same anonymous pages. This paper presents Sparrow, a similar-page aware deduplication solution for mobile systems. Sparrow shows that memory pages still have the potential to deduplicate, even though the same pages are rare. An interesting observation inspires this, that is, a high number of pages having the partially-same contents. We have implemented Sparrow on real-life smartphones. Experimental results indicate that 30.45% more space can be saved with Sparrow.

关键词： Degradation Memory management User experience Mobile applications Servers Smart phones

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CacheTrimmer: Adaptive Cache File Trimming for Optimized Performance and Lifetime on Mobile Devices

CacheTrimmer: Adaptive Cache File Trimming for Optimized Per...

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IEEE International conference on computer design: VLSI in computers and Processors, (ICCD)

作者： Yi Zhang Yunpeng Song Wentong Li Yiyang Huang Dingcui Yu Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Mobile devices always cache numerous files during application runtime, which can be trimmed to improve the user experience. However, existing cache file trimming methods are unaware of the cleaning cost within the file system and storage devices, which degrades the system performance and storage lifetime, resulting in low benefits of trimming cache files. Motivated by this, an adaptive cache file trimming (CacheTrimmer) scheme is proposed to trim cache files for performance and lifetime improvement. The basic idea is to determine the trimming timing based on the cleaning cost of the file system and storage device, maximizing the benefit of trimming cache files. Specifically, CacheTrimmer includes two components: First, a cleaning cost-aware trimming method is proposed to trim cache files by recording the index information of cache files in a list and determining the timing and size of file trimming. Second, to avoid trimming-induced intra-segment fragmentation and improve trimming efficiency, a log-structured cache scheme is further proposed to maintain the cache files in separate segments. We prototype CacheTrimmer with a real mobile platform. Experimental results under real workloads show that CacheTrimmer achieves encourage performance and lifetime improvement compared to the state-of-the-art.

关键词： Performance evaluation costs Runtime File systems System performance Cleaning Mobile handsets User experience Timing Recording

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Static code Analysis of IEC 61131–3 ST Programs via Symbolic Execution

Static Code Analysis of IEC 61131–3 ST Programs via Symboli...

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IEEE International conference on Systems, Man and Cybernetics

作者： Mengyan Zhao Yanhong Huang Jianqi Shi Yinghao Chen Yang Yang East China Normal University Shanghai China National Trusted Embedded Software Engineering Technology Research Center Shanghai China Hardware/Software Co-Design Technology and Application Engineering Research Center Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

A Programmable Logic controller (PLC) is an essentially domain-specific computer used to control physical equipment and is widely used in industrial control fields. It plays a crucial role in automating complex processes for industrial automation systems, requiring high reliability as code vulnerabilities can potentially lead to disasters. Therefore, vulnerability detection in PLC programs is of significant importance. However, the availability of tools supporting vulnerability detection in PLC programming languages is limited. This paper attempts to improve industrial security from the perspective of code security and proposes a static code analysis approach specifically designed for IEC 61131–3 Structured Text (ST) programs. This approach uses structural pattern matching and symbolic execution technology to identify program defects and improve quality by detecting problematic code structures and potential issues early in the development process, thereby reducing the debugging effort required during developments. considering the characteristic of periodic loop execution in PLCs, we introduce the loop unwinding technique to collect constraints from subsequent execution cycles for detection purposes. Based on the aforementioned approach, we implement a static code analysis tool, ST-Checker and make a series of evaluations. The experimental results show that this method is feasible and can detect potential defects that existing PLC compilers cannot detect, improving the precision of defect detection with data dependencies.

关键词： codes Industrial control Programmable logic devices Merging Focusing Security Reliability IEC Standards Pattern matching Defect detection

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Protein Functional Family Classification Based on Multilevel Feature Information

Protein Functional Family Classification Based on Multilevel...

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2022 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhou, Guokai Chen, Weiting East China Normal University MOE Research Center of Software/Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9781665468190

Next-generation sequencing technologies generated a large number of new sequenced proteins. Classifying these proteins into functional families is an important task in the field of biological protein property research. The high-cost and time-consuming drawbacks of bioassays promote the development of machine learning-based protein classification studies. However, previous work only used single level features, which were not informative enough and had classification bias. In this paper, we propose a new model named PCNM to alleviate the above problems. Different from previous work that extracts only a single level of features, the PCNM model uses a convolutional Neural Network (CNN) module and a Global and Local feature (GL) module to extract feature at three levels: conserved domains, amino acids, and protein sequences. The experimental results of the Phages of orthologous groups dataset (POG) and the G-protein coupled receptor superfamily dataset (GPCR) show that our proposed PCNM outperforms currently available methods in five metrics: accuracy, precision, recall, F1 score, and MCC. PCNM achieves a high accuracy of 85.12% on the GPCR sub-subfamily dataset and 81.42% on the POG dataset. The robustness experiments of PCNM further demonstrate that the model alleviates the classification bias. © 2022 IEEE.

关键词： Proteins

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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A Heatmap-Based Weighted Multi-stream Fusion Network in Skeleton Modality for Action Recognition

A Heatmap-Based Weighted Multi-stream Fusion Network in Skel...

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Intelligent control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International conference on

作者： Xinlong Shi Wenjie Liu Xianzhong Liu Software Engineering Institute East China Normal University Shanghai China Software/Hardware Co-Design Engineering Research Center MoE Shanghai China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Human Action Recognition (HAR) has received widespread attention in recent years. For skeleton modality, the representation of spatio-temporal motion and the weight allocation of different streams are still under discussion. Therefore, we propose a Heatmap-based Weighted Multi-Stream Fusion Network, HW-MSFN, which attempts to represent spatiotemporal motion in a novel way, prompting the adaptability of 3D-CNN to motion. Additionally, compared with the slow motion obtained frame by frame, we use fast motion which is captured at a larger time scale, in order to attain more general motion characteristics. Finally, to make full use of inter-frame information and intra-frame information, we explore the best weight allocation of multiple stream fusion for skeleton modality. Experiments implemented on 3 databases including HMDB51, UCF101 and FineGYM99 show that our model outperforms state-of-the-art methods in single skeleton modality.

关键词： Heating systems Adaptation models Signal processing Feature extraction Skeleton Spatiotemporal phenomena Resource management Human activity recognition Streams Intelligent control

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An efficient group key agreement protocol with administrator 3

An efficient group key agreement protocol with administrator

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3rd International conference on Internet of Things and Smart City, IoTSC 2023

作者： Fang, Qihao Zhang, Lei Zhang, Mingrui Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Chengdu610041 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education Shanghai200062 China

ISBN: (纸本)9781510666375

Group key agreement technology is one of the most popular solution to establish a reliable secure channel between communicating entities of a group. Group key agreement protocol with administrator (GKAA) can handle a large number of group operations in sequence to resolve disorder problem and it is widely used in practical applications. However, in most of existing protocol, the administrator has to compute materials for each participant in the group which brings huge calculate overhead. In this paper, we propose an efficient group key agreement protocol with Administrator. Our GKAA protocol is able to perform group operation in a high efficacy way with the help of a key tree. It is also flexible and scalable since it suits for any network communication environment like IoT, video meeting, social applications, etc. Our protocol satisfies the properties including forward secrecy, PCS and key control resistance. © 2023 SPIE.

关键词： Network security

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