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Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

IEEE Transactions on Network Science and engineering 2025年第3期12卷 2081-2092页

作者： Wen, Yao Zhang, Guopeng Wang, Kezhi Yang, Kun China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Brunel University London Department of Computer Science MiddlesexUB8 3PH United Kingdom Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework. © 2013 IEEE.

关键词： Federated learning

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Migrant Resettlement by Evolutionary Multiobjective Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 51-65页

作者： Liu, Dan-Xuan Gu, Yu-Ran Qian, Chao Mu, Xin Tang, Ke Nanjing University National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing210023 China Peng Cheng Laboratory Shenzhen518000 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this article, we propose a new framework called migrant resettlement by evolutionary multiobjective optimization (MR-EMO), which reformulates migrant resettlement as a biobjective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a multiobjective evolutionary algorithm (MOEA) to solve the biobjective problem. We implement MR-EMO using three MOEAs: the popular nondominated sorting genetic algorithm II (NSGA-II), MOEA based on decomposition (MOEA/D) as well as the theoretically grounded global simple evolutionary multiobjective optimizer (GSEMO). To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO using matrix-swap mutation and repair mechanism (GSEMO-SR), which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO. © 2024 IEEE.

关键词： Multiobjective optimization

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CorLMI-FSA: An Efficient Feature Selection Approach for Stress Classification Using Physiological Signals 5

CorLMI-FSA: An Efficient Feature Selection Approach for Stre...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798350356236

Mental stress in academic settings significantly impacts students' health and performance. Physiological signals such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV) offer valuable insights for stress classification. However, the multidomain nature of these signals often introduces redundancy and irrelevant data, reducing both classification accuracy and computational efficiency [1]. Hence, this paper proposes an efficient feature selection algorithm, CorLMI-FSA (Correlation-Logistic Mutual Information Feature Selection Algorithm), which dynamically adjusts redundancy weights through a logistic function to capture nonlinear dependencies among features. The proposed algorithm achieves accuracies of 96.82% and 95.84% for binary and three-level stress classification tasks using Gradient Boosting (GB), enhancing computational efficiency by selecting only the most relevant features. These results underscore CorLMI-FSA's potential as a low-complexity solution for realtime stress monitoring in wearable devices, facilitating timely intervention. © 2025 IEEE.

关键词： Stress analysis

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Breast Cancer Detection Using a New Parallel Hybrid Logistic Regression Model Trained by Particle Swarm Optimization and Clonal Selection Algorithms

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Etcil, Mustafa Dedeturk, Bilge Kagan Kolukisa, Burak Bakir-Gungor, Burcu Gungor, Vehbi Cagri Department of Computer Engineering Abdullah Gül University Kayseri Turkey Department of Software Engineering Erciyes University Kayseri Turkey Department of Software Engineering Kayseri University Kayseri Turkey Turkcell Istanbul Turkey

Breast cancer is one of the most widespread kinds of cancer, especially in women, and it has a high mortality rate. With the help of technology, it is possible to develop a computer-aided method for the diagnosis of breast cancer, which is crucial for effective treatment. Recent breast cancer diagnosis studies utilizing numerous machine learning models were efficient and innovative. However, it has been observed that they may have problems such as long training times and low accuracy rates. To this end, in this study, we present a new classifier that utilizes a hybrid of the clonal selection algorithm (CSA) and the particle swarm optimization (PSO) algorithm for the training of the logistic regression (LR) model, which is named CSA-PSO-LR. The proposed method is evaluated using two publicly accessible breast cancer datasets, that is, the Wisconsin Diagnostic Breast Cancer (WDBC) database and the Wisconsin Breast Cancer Database (WBCD), with 10-fold cross-validation and Bayesian hyperparameter optimization techniques. Additionally, a CPU parallelization method is applied, which substantially shortens the training time of the model. The efficacy of the CSA-PSO-LR classifier is compared with state-of-the-art machine learning algorithms and related studies in the literature. Performance analysis indicates that the proposed method achieves 98.75% accuracy and 98.27% F1-score on the WDBC dataset, and 97.94% accuracy and 97.35% F1-score on the WBCD dataset. These results demonstrate the potential of the proposed method as an effective approach for improving breast cancer diagnosis. © 2025 The Author(s). Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Particle swarm optimization (PSO)

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A Trustworthy Analysis Approach for Chatbots on Health Data: ChatGPT-4 Example

A Trustworthy Analysis Approach for Chatbots on Health Data:...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Kübra Arslanoğlu Mehmet Karaköse The Department of Software Engineering The Faculty of Engineering The University of Firat Elazig The Department of Computer Engineering The Faculty of Engineering The University of Firat Elazig

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Considering the increasing and widespread use of chatbots, it is of great importance to provide methods and tools to address ethical concerns and to make users aware of various aspects of a chatbot, including non-functional features. The Generative Pre-Trained Transformer (GPT) is a state-of-the-art Natural Language Generation (NLG) model, enhanced by the latest version of GPT-4. This study evaluates the trustworthiness of the responses of three different chatbot models, BlenderBot with Hugging Face, ChatGPT-3.5 and ChatGPT-4, to 100 randomly selected medical questions. The accuracy and semantic similarity of the answers were measured by BLEU, ROUGE-1 and Cosine Similarity metrics, and response times were also recorded as an important performance factor. The results showed that GPT-4 exhibited superior performance, thus being able to produce more accurate and contextually reliable responses. However, the significantly longer response time of GPT-4 emerged as a disadvantage that may affect real-time utilisation. These findings provide an important reference for the effective use of GPT-4 in the context of health chatbots. The study contributes to the literature on improving the effectiveness of chatbots in healthcare by drawing attention to the importance of balancing speed, accuracy and trustworthiness, as well as the fact that the answers obtained are drawn with Application Programming Interface (API).

关键词： Accuracy Natural language generation Semantics Medical services Chatbots Transformers Real-time systems Time factors Reliability Application programming interfaces

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Topology entropy: Enhancing graph partitioning for TAD identification and single-cell clustering

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Computational and Structural Biotechnology Journal 2025年 27卷 1864-1886页

作者： Liang, Qiushi Zhao, Shengjie Chen, Lingxi Li, Shuai Cheng School of Computer Science and Technology Tongji University Shanghai201804 China Department of Computer Science City University of Hong Kong 999077 Hong Kong Department of Biomedical Sciences City University of Hong Kong 999077 Hong Kong Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education China

Entropy quantifies the limits of information compression and provides a theoretical foundation for exploring complex structures in large-scale graphs. However, effective metrics are needed to capture the intricate structural details in biological graphs. In this paper, we introduce the topology entropy encoding tree to quantify the complexity of biological graphs and show that minimizing the associated entropy is equivalent to optimal graph partitioning. We develop two methods, TEC-O and TEC-U, for partitioning ordered and unordered biological graphs. TEC-O is applied to identify Topologically Associated Domains (TADs) in Hi-C contact maps, while TEC-U is used for cell clustering in single-cell sequencing data. Results from simulated datasets demonstrate that topology entropy is robust to noise and effectively captures structural information, outperforming existing methods. Experiments on Hi-C data from five cell lines and ten single-cell sequencing datasets show that TEC-O and TEC-U achieve the highest accuracy in TAD detection and cell clustering, respectively, providing biologically meaningful insights. © 2025 The Authors

关键词： Cell culture

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VGGBM-Net: A Novel Pixel-Based Transfer Features engineering for Automated Coffee Bean Diseases Classification

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IEEE Access 2025年 13卷 83580-83590页

作者： Hashmi, Muhammad Shadab Alam Qadri, Azam Mehmood Raza, Ali Ullah, Saleem Smerat, Aseel Kim, Changgyun Syafrudin, Muhammad Fitriyani, Norma Latif Khwaja Fareed University of Engineering and Information Technology Department of Computer Science Rahim Yar Khan Pakistan University of Lahore Department of Software Engineering Lahore 54000 Pakistan Al-Ahliyya Amman University Faculty of Educational Sciences Amman 19328 Jordan Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Chitkara University Punjab Rajpura 140401 India Mazaya University College Computer Technologies Engineering Nasiriyah Iraq Kangwon National University Department of Artificial Intelligence & Software Samcheok 25913 South Korea Sejong University Department of Artificial Intelligence and Data Science Seoul 05006 South Korea

Although coffee is a crucial commodity for the economies of developing nations, defects in processing can significantly impact the safety, commercial value, and quality of coffee beans. Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. This study proposes a novel transfer learning-based deep neural network, VGGBM-Net, for the classification of coffee bean diseases. It focuses on the timely and precise identification of diseases to improve export quality without compromising safety during production. In this research, the proposed approach is trained using a dataset USK-Coffee of coffee beans to classify healthy and unhealthy beans. The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models this feature extraction enhances classification accuracy and robustness. This transformation improves feature extraction by capturing more discriminative patterns, leading to superior performance compared to conventional methods. Experimental results highlight the superior performance of the LGBM classifier, achieving an impressive 99% accuracy, recall, f1, and precision score of 98% with a computational runtime of just 0.084 seconds. K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. This research establishes a highly effective fra

关键词： Coffee Bean Disease Deep Learning Feature engineering Image Processing Transfer Learning

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Batch Execution of Microbenchmarks for Efficient Performance Testing

Batch Execution of Microbenchmarks for Efficient Performance...

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International Conference on software Testing, Verification, and Validation, ICST

作者： Mostafa Jangali Kundi Yao Yiming Tang Diego Elias Costa Weiyi Shang Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Software Engineering Rochester Institute of Technology USA Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Performance microbenchmarking is essential for ensuring software quality by providing granular insights into code efficiency. While automated performance microbenchmark generation tools (e.g., ju2jmh) are proposed to alleviate practitioners from manually curating microbenchmarks, the high volume of generated benchmarks can lead to protracted benchmarking execution time, as many of the generated benchmarks are too short in nature to be valuable for evaluating performance. In this paper, we present a novel approach that optimizes microbenchmark execution through a batching strategy, i.e., grouping benchmarks with similar code coverage and treating them as a single unit to 1) reduce execution overhead and 2) reduce the bias from microbenchmarks that are too short. We evaluate the effectiveness of this enhancement across various Java projects, comparing the execution times of clustered and individual micro benchmarks. Our findings demonstrate substantial improvements in execution efficiency, reducing execution time by up to 89.81% while preserving high microbenchmark stability.

关键词： software testing Java Codes software quality software performance Benchmark testing

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Understanding and Identifying Technical Debt in the Co-Evolution of Production and Test Code

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IEEE Transactions on software engineering 2025年第5期51卷 1415-1436页

作者： Guo, Yimeng Chen, Zhifei Xiao, Lu Chen, Lin Li, Yanhui Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Stevens Institute of Technology Department of Systems and Enterprises Charles V. Schaefer Jr. School of Engineering and Science HobokenNJ07030 United States

The co-evolution of production and test code (PT co-evolution) has received increasing attention in recent years. However, we found that existing work did not comprehensively study various PT co-evolution scenarios, such as the qualification and persistence of their effects on software. Inspired by technical debt (TD), we refer to TD generated during the co-evolution between production and test code as PT co-evolution technical debt (PTCoTD). To better understand PT co-evolution, we first conducted an exploratory study on its characteristics on 15 open-source projects, finding unbalanced PT co-evolution prevalent and summarizing five potential PT flaws. Then we proposed an approach to identify and quantify PTCoTDs of these flaw patterns, considering evolutionary and structural relationships. We also built prediction models to describe cost trajectories and rank all PTCoTDs to prioritize expensive ones. The evaluation on the 15 projects shows that our approach can identify PTCoTDs that deserve attention. The identified PTCoTDs account for about half of the project's total maintenance costs, and the cost proportion of the expensive Top-5 is 1.8x more than the file proportion they contain. Almost all covered maintenance costs persist as PTCoTD in the future, with an average increase of 6.8% between the last two releases. Our approach also accurately predicts the costs of PTCoTD with an average prediction deviation of only 8.3%. Our study provides valuable insights into PT co-evolution scenarios and their effects, which can guide practices and inspire future work on software testing and maintenance. © 1976-2012 IEEE.

关键词： software testing

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Enhancing Autonomous Driving with Pre-trained Imitation and Reinforcement Learning

Enhancing Autonomous Driving with Pre-trained Imitation and ...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Choi, Jeong-Hwan Kim, Dong-Han Yoo, Ji-Sang Kim, Beom-Joon Hwang, Jun-Tae DongSeoul University Department of Electronic Engineering Seongnam Korea Republic of Kyung Hee University Department of Computer Engineering Yongin Korea Republic of Kyung Hee University Department of Artificial Intelligence Yongin Korea Republic of Kyung Hee University Department of Software Convergence Yongin Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a method that combines imitation learning and reinforcement learning to improve the learning performance of autonomous vehicles. The actor model was pre-trained using expert demonstration data from the CARLA simulator, and then integrated into PPO-based reinforcement learning. The experimental results showed that the model combining imitation learning outperformed the model using only reinforcement learning, particularly in the early stages of training. However, this study is limited by using only image data, and future research will incorporate vehicle position and speed information for enhanced results. © 2025 IEEE.

关键词： Adversarial machine learning

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