Pub Date : 2024-04-02DOI: 10.1109/TETCI.2024.3367811
Jie Yang;Chin-Teng Lin
Hierarchical clustering is able to provide partitions of different granularity levels. However, most existing hierarchical clustering techniques perform clustering in the original feature space of the data, which may suffer from overlap, sparseness, or other undesirable characteristics, resulting in noncompetitive performance. In the field of deep clustering, learning representations using pseudo labels has recently become a research hotspot. Yet most existing approaches employ coarse-grained pseudo labels, which may contain noise or incorrect labels. Hence, the learned feature space does not produce a competitive model. In this paper, we introduce the idea of fine-grained labels of supervised learning into unsupervised clustering, giving rise to the enhanced adjacency-constrained hierarchical clustering (ECHC) model. The full framework comprises four steps. One, adjacency-constrained hierarchical clustering (CHC) is used to produce relatively pure fine-grained pseudo labels. Two, those fine-grained pseudo labels are used to train a shallow multilayer perceptron to generate good representations. Three, the corresponding representation of each sample in the learned space is used to construct a similarity matrix. Four, CHC is used to generate the final partition based on the similarity matrix. The experimental results show that the proposed ECHC framework not only outperforms 14 shallow clustering methods on eight real-world datasets but also surpasses current state-of-the-art deep clustering models on six real-world datasets. In addition, on five real-world datasets, ECHC achieves comparable results to supervised algorithms.
{"title":"Enhanced Adjacency-Constrained Hierarchical Clustering Using Fine-Grained Pseudo Labels","authors":"Jie Yang;Chin-Teng Lin","doi":"10.1109/TETCI.2024.3367811","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3367811","url":null,"abstract":"Hierarchical clustering is able to provide partitions of different granularity levels. However, most existing hierarchical clustering techniques perform clustering in the original feature space of the data, which may suffer from overlap, sparseness, or other undesirable characteristics, resulting in noncompetitive performance. In the field of deep clustering, learning representations using pseudo labels has recently become a research hotspot. Yet most existing approaches employ coarse-grained pseudo labels, which may contain noise or incorrect labels. Hence, the learned feature space does not produce a competitive model. In this paper, we introduce the idea of fine-grained labels of supervised learning into unsupervised clustering, giving rise to the enhanced adjacency-constrained hierarchical clustering (ECHC) model. The full framework comprises four steps. One, adjacency-constrained hierarchical clustering (CHC) is used to produce relatively pure fine-grained pseudo labels. Two, those fine-grained pseudo labels are used to train a shallow multilayer perceptron to generate good representations. Three, the corresponding representation of each sample in the learned space is used to construct a similarity matrix. Four, CHC is used to generate the final partition based on the similarity matrix. The experimental results show that the proposed ECHC framework not only outperforms 14 shallow clustering methods on eight real-world datasets but also surpasses current state-of-the-art deep clustering models on six real-world datasets. In addition, on five real-world datasets, ECHC achieves comparable results to supervised algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.1109/TETCI.2024.3377728
Jingke Yan;Yao Cheng;Qin Wang;Lei Liu;Weihua Zhang;Bo Jin
Thanks to the development of deep learning, machine abnormal sound detection (MASD) based on unsupervised learning has exhibited excellent performance. However, in the task of unsupervised MASD, there are discrepancies between the acoustic characteristics of the test set and the training set under the physical parameter changes (domain shifts) of the same machine's operating conditions. Existing methods not only struggle to stably learn the sound signal features under various domain shifts but also inevitably increase computational overhead. To address these issues, we propose an unsupervised machine abnormal sound detection model based on Transformer and Dynamic Graph Convolution (Unsuper-TDGCN) in this paper. Firstly, we design a network that models time-frequency domain features to capture both global and local spatial and time-frequency interactions, thus improving the model's stability under domain shifts. Then, we introduce a Dynamic Graph Convolutional Network (DyGCN) to model the dependencies between features under domain shifts, enhancing the model's ability to perceive changes in domain features. Finally, a Domain Self-adaptive Network (DSN) is employed to compensate for the performance decline caused by domain shifts, thereby improving the model's adaptive ability for detecting anomalous sounds in MASD tasks under domain shifts. The effectiveness of our proposed model has been validated on multiple datasets.
{"title":"Transformer and Graph Convolution-Based Unsupervised Detection of Machine Anomalous Sound Under Domain Shifts","authors":"Jingke Yan;Yao Cheng;Qin Wang;Lei Liu;Weihua Zhang;Bo Jin","doi":"10.1109/TETCI.2024.3377728","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377728","url":null,"abstract":"Thanks to the development of deep learning, machine abnormal sound detection (MASD) based on unsupervised learning has exhibited excellent performance. However, in the task of unsupervised MASD, there are discrepancies between the acoustic characteristics of the test set and the training set under the physical parameter changes (domain shifts) of the same machine's operating conditions. Existing methods not only struggle to stably learn the sound signal features under various domain shifts but also inevitably increase computational overhead. To address these issues, we propose an unsupervised machine abnormal sound detection model based on Transformer and Dynamic Graph Convolution (Unsuper-TDGCN) in this paper. Firstly, we design a network that models time-frequency domain features to capture both global and local spatial and time-frequency interactions, thus improving the model's stability under domain shifts. Then, we introduce a Dynamic Graph Convolutional Network (DyGCN) to model the dependencies between features under domain shifts, enhancing the model's ability to perceive changes in domain features. Finally, a Domain Self-adaptive Network (DSN) is employed to compensate for the performance decline caused by domain shifts, thereby improving the model's adaptive ability for detecting anomalous sounds in MASD tasks under domain shifts. The effectiveness of our proposed model has been validated on multiple datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.1109/TETCI.2024.3378599
Ling Huang;Can-Rong Guan;Zhen-Wei Huang;Yuefang Gao;Chang-Dong Wang;C. L. P. Chen
Recently, Deep Neural Networks (DNNs) have been largely utilized in Collaborative Filtering (CF) to produce more accurate recommendation results due to their ability of extracting the nonlinear relationships in the user-item pairs. However, the DNNs-based models usually encounter high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we develop a novel broad recommender system named Broad Collaborative Filtering (BroadCF), which is an efficient nonlinear collaborative filtering approach. Instead of DNNs, Broad Learning System (BLS) is used as a mapping function to learn the nonlinear matching relationships in the user-item pairs, which can avoid the above issues while achieving very satisfactory rating prediction performance. Contrary to DNNs, BLS is a shallow network that captures nonlinear relationships between input features simply and efficiently. However, directly feeding the original rating data into BLS is not suitable due to the very large dimensionality of the original rating vector. To this end, a new preprocessing procedure is designed to generate user-item rating collaborative vector, which is a low-dimensional user-item input vector that can leverage quality judgments of the most similar users/items. Convincing experimental results on seven datasets have demonstrated the effectiveness of the BroadCF algorithm.
{"title":"Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach","authors":"Ling Huang;Can-Rong Guan;Zhen-Wei Huang;Yuefang Gao;Chang-Dong Wang;C. L. P. Chen","doi":"10.1109/TETCI.2024.3378599","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3378599","url":null,"abstract":"Recently, Deep Neural Networks (DNNs) have been largely utilized in Collaborative Filtering (CF) to produce more accurate recommendation results due to their ability of extracting the nonlinear relationships in the user-item pairs. However, the DNNs-based models usually encounter high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we develop a novel broad recommender system named Broad Collaborative Filtering (BroadCF), which is an efficient nonlinear collaborative filtering approach. Instead of DNNs, Broad Learning System (BLS) is used as a mapping function to learn the nonlinear matching relationships in the user-item pairs, which can avoid the above issues while achieving very satisfactory rating prediction performance. Contrary to DNNs, BLS is a shallow network that captures nonlinear relationships between input features simply and efficiently. However, directly feeding the original rating data into BLS is not suitable due to the very large dimensionality of the original rating vector. To this end, a new preprocessing procedure is designed to generate user-item rating collaborative vector, which is a low-dimensional user-item input vector that can leverage quality judgments of the most similar users/items. Convincing experimental results on seven datasets have demonstrated the effectiveness of the BroadCF algorithm.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The robustness of neural networks in image classification is important to resist adversarial attacks. Although many researchers proposed to enhance the network robustness by inventing network training paradigms or designing network architectures, existing approaches are mainly based on a single type of networks, e.g., convolution neural networks (CNNs) or vision Transformer (ViT). Considering a recently revealed fact that CNNs and ViT can effectively defend against adversarial attacks transferred from each other, this paper aims to enhance network robustness by designing robust hybrid architecture networks containing different types of networks. To this end, we propose a hybrid architecture-based evolutionary neural architecture search approach for robust architecture design, termed HA-ENAS. Specifically, to combine or aggregate different types of networks in the same network framework, a multi-stage block-wise hybrid architecture network is first devised as the supernet, where three types of blocks (called convolution blocks, Transformer blocks, multi-layer perception blocks) are further designed as each block's candidate, and thus a hybrid architecture-based search space is established for HA-ENAS; then, the robust hybrid architecture search is formulated as an optimization problem maximizing both clean and adversarial accuracy of architectures, and an efficient multi-objective evolutionary algorithm is employed to solve the problem, where a supernet-based retraining evaluation and a surrogate model are used to mitigate coupled weight influence and reduce the whole search cost. Experimental results show that the hybrid architectures found by the proposed HA-ENAS outperform state-of-the-art single-type architectures in terms of clean accuracy and adversarial accuracy under a variety of common attacks.
在图像分类中，神经网络的鲁棒性对于抵御恶意攻击非常重要。尽管许多研究人员提出通过发明网络训练范式或设计网络架构来增强网络的鲁棒性，但现有方法主要基于单一类型的网络，如卷积神经网络（CNN）或视觉转换器（ViT）。考虑到最近揭示的一个事实，即 CNN 和 ViT 可以有效抵御相互转移的对抗性攻击，本文旨在通过设计包含不同类型网络的鲁棒混合架构网络来增强网络的鲁棒性。为此，我们提出了一种基于混合架构的鲁棒架构设计进化神经架构搜索方法，称为 HA-ENAS。具体来说，为了在同一网络框架中组合或聚合不同类型的网络，我们首先设计了一个多阶段分块式混合架构网络作为超级网络，并进一步设计了三种类型的分块（称为卷积分块、变换器分块和多层感知分块）作为每个分块的候选，从而为 HA-ENAS 建立了一个基于混合架构的搜索空间；然后，将鲁棒混合架构搜索表述为一个优化问题，使架构的清洁度和对抗精度都最大化，并采用高效的多目标进化算法来解决该问题，其中基于超网的再训练评估和代理模型用于减轻耦合权重的影响并降低整个搜索成本。实验结果表明，在各种常见攻击下，HA-ENAS 所发现的混合架构在净精度和对抗精度方面都优于最先进的单一类型架构。
{"title":"Hybrid Architecture-Based Evolutionary Robust Neural Architecture Search","authors":"Shangshang Yang;Xiangkun Sun;Ke Xu;Yuanchao Liu;Ye Tian;Xingyi Zhang","doi":"10.1109/TETCI.2024.3400867","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3400867","url":null,"abstract":"The robustness of neural networks in image classification is important to resist adversarial attacks. Although many researchers proposed to enhance the network robustness by inventing network training paradigms or designing network architectures, existing approaches are mainly based on a single type of networks, e.g., convolution neural networks (CNNs) or vision Transformer (ViT). Considering a recently revealed fact that CNNs and ViT can effectively defend against adversarial attacks transferred from each other, this paper aims to enhance network robustness by designing robust hybrid architecture networks containing different types of networks. To this end, we propose a hybrid architecture-based evolutionary neural architecture search approach for robust architecture design, termed HA-ENAS. Specifically, to combine or aggregate different types of networks in the same network framework, a multi-stage block-wise hybrid architecture network is first devised as the supernet, where three types of blocks (called convolution blocks, Transformer blocks, multi-layer perception blocks) are further designed as each block's candidate, and thus a hybrid architecture-based search space is established for HA-ENAS; then, the robust hybrid architecture search is formulated as an optimization problem maximizing both clean and adversarial accuracy of architectures, and an efficient multi-objective evolutionary algorithm is employed to solve the problem, where a supernet-based retraining evaluation and a surrogate model are used to mitigate coupled weight influence and reduce the whole search cost. Experimental results show that the hybrid architectures found by the proposed HA-ENAS outperform state-of-the-art single-type architectures in terms of clean accuracy and adversarial accuracy under a variety of common attacks.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.1109/TETCI.2024.3378605
Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang
This paper considers a secure satellite downlink communication system with a hybrid intelligent reflecting surface (IRS). A robust design problem for the satellite and IRS joint beamforming is formulated to maximize the system's worst-case secrecy rate, considering practical models of the outdated channel state information and IRS power consumption. We leverage deep reinforcement learning (DRL) to solve the problem by proposing a fast DRL algorithm, namely the deep post-decision state–deterministic policy gradient (DPDS-DPG) algorithm. In DPDS-DPG, the prior known system dynamics are exploited by integrating the PDS concept into the traditional deep DPG (DDPG) algorithm, resulting in faster learning convergence. Simulation results show a faster learning convergence of 50% for DPDS-DPG compared to DDPG, with a comparable achievable system secrecy rate. Additionally, the results demonstrate system secrecy rate gains of 52% and 35% when employing active IRS and hybrid IRS, respectively, over conventional passive IRS, thereby supporting secure communications.
{"title":"Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach","authors":"Quynh Tu Ngo;Khoa Tran Phan;Abdun Mahmood;Wei Xiang","doi":"10.1109/TETCI.2024.3378605","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3378605","url":null,"abstract":"This paper considers a secure satellite downlink communication system with a hybrid intelligent reflecting surface (IRS). A robust design problem for the satellite and IRS joint beamforming is formulated to maximize the system's worst-case secrecy rate, considering practical models of the outdated channel state information and IRS power consumption. We leverage deep reinforcement learning (DRL) to solve the problem by proposing a fast DRL algorithm, namely the deep post-decision state–deterministic policy gradient (DPDS-DPG) algorithm. In DPDS-DPG, the prior known system dynamics are exploited by integrating the PDS concept into the traditional deep DPG (DDPG) algorithm, resulting in faster learning convergence. Simulation results show a faster learning convergence of 50% for DPDS-DPG compared to DDPG, with a comparable achievable system secrecy rate. Additionally, the results demonstrate system secrecy rate gains of 52% and 35% when employing active IRS and hybrid IRS, respectively, over conventional passive IRS, thereby supporting secure communications.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.1109/TETCI.2024.3369319
Zhiyuan Yang;Qingfu Zhang
2D GANs have yielded impressive results especially in image synthesis. However, they often encounter challenges with multi-view inconsistency due to the absence of 3D perception in their generation process. To overcome this shortcoming, 3D-aware GANs have been proposed to take advantage of both 3D representation methods, GANs, but it is very difficult to edit semantic attributes. To explore the semantic disentanglement in the 3D-aware latent space, this paper proposes a general framework, presents two representative approaches for the 3D manipulation task in both supervised, unsupervised manners. Our key idea is to utilize existing latent discovery methods, bring direct compatibility to 3D control. Specifically, we propose a novel module to extract the semantic latent space of the existing 3D-aware models, then develop two approaches to find a normal editing direction in the latent space. Leveraging the meaningful semantic latent directions, we can easily edit the shape, appearance attributes while preserving the 3D consistency. Quantitative, qualitative experiments show that our method is effective, efficient for the 3D-aware generation with steerability on both synthetic, real-world datasets.
二维 GAN 取得了令人瞩目的成果，尤其是在图像合成方面。然而，由于在生成过程中缺乏三维感知，它们经常会遇到多视图不一致的难题。为了克服这一缺陷，人们提出了三维感知 GAN，以利用三维表示方法和 GAN 的优势，但编辑语义属性非常困难。为了探索三维感知潜空间中的语义分解问题，本文提出了一个总体框架，并针对三维操作任务提出了两种有监督和无监督的代表性方法。我们的主要想法是利用现有的潜在发现方法，直接兼容三维控制。具体来说，我们提出了一个新模块来提取现有三维感知模型的语义潜空间，然后开发了两种方法来寻找潜空间中的法线编辑方向。利用有意义的语义潜在方向，我们可以轻松地编辑形状、外观属性，同时保持三维一致性。定量和定性实验表明，我们的方法在合成和真实世界数据集上都能有效、高效地生成具有可转向性的三维感知模型。
{"title":"Discovering Interpretable Latent Space Directions for 3D-Aware Image Generation","authors":"Zhiyuan Yang;Qingfu Zhang","doi":"10.1109/TETCI.2024.3369319","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369319","url":null,"abstract":"2D GANs have yielded impressive results especially in image synthesis. However, they often encounter challenges with multi-view inconsistency due to the absence of 3D perception in their generation process. To overcome this shortcoming, 3D-aware GANs have been proposed to take advantage of both 3D representation methods, GANs, but it is very difficult to edit semantic attributes. To explore the semantic disentanglement in the 3D-aware latent space, this paper proposes a general framework, presents two representative approaches for the 3D manipulation task in both supervised, unsupervised manners. Our key idea is to utilize existing latent discovery methods, bring direct compatibility to 3D control. Specifically, we propose a novel module to extract the semantic latent space of the existing 3D-aware models, then develop two approaches to find a normal editing direction in the latent space. Leveraging the meaningful semantic latent directions, we can easily edit the shape, appearance attributes while preserving the 3D consistency. Quantitative, qualitative experiments show that our method is effective, efficient for the 3D-aware generation with steerability on both synthetic, real-world datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.1109/TETCI.2024.3377151
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2024.3377151","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377151","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.1109/TETCI.2024.3377153
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2024.3377153","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377153","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.1109/TETCI.2024.3371222
Sadaf Naz;Khoa Phan;Yi-Ping Phoebe Chen
In the health domain, due to privacy issues, many important datasets are isolated, which nonetheless need to be analyzed collaboratively for conclusions to be drawn efficiently. To maintain data privacy, federated learning (FL) trains a communal model from scattered datasets without centralized data integration. In this paper, we compare and analyze the performance of traditional deep learning (DL) and FL techniques using the chest X-Ray (CXR) image dataset for COVID-19 detection. We first implemented DL techniques VGG-16, ResNet50, and Inceptionv3, where ResNet50 is found to be best on the classification task with 98% accuracy. We then proposed FL implementations - federated averaging and federated learning using ResNet50 for training local and global models. The proposed FL converges faster and outperforms the base FL for both independent and identically distributed (IID) and non-IID datasets. While the FL handles bigger data efficiently, compared to DL, it compromised 3.56% in accuracy to preserve privacy. Our results provide a platform for the further investigation of FL in COVID-19 detection.
{"title":"Centralized and Federated Learning for COVID-19 Detection With Chest X-Ray Images: Implementations and Analysis","authors":"Sadaf Naz;Khoa Phan;Yi-Ping Phoebe Chen","doi":"10.1109/TETCI.2024.3371222","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3371222","url":null,"abstract":"In the health domain, due to privacy issues, many important datasets are isolated, which nonetheless need to be analyzed collaboratively for conclusions to be drawn efficiently. To maintain data privacy, federated learning (FL) trains a communal model from scattered datasets without centralized data integration. In this paper, we compare and analyze the performance of traditional deep learning (DL) and FL techniques using the chest X-Ray (CXR) image dataset for COVID-19 detection. We first implemented DL techniques VGG-16, ResNet50, and Inceptionv3, where ResNet50 is found to be best on the classification task with 98% accuracy. We then proposed FL implementations - federated averaging and federated learning using ResNet50 for training local and global models. The proposed FL converges faster and outperforms the base FL for both independent and identically distributed (IID) and non-IID datasets. While the FL handles bigger data efficiently, compared to DL, it compromised 3.56% in accuracy to preserve privacy. Our results provide a platform for the further investigation of FL in COVID-19 detection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-27DOI: 10.1109/TETCI.2024.3377155
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3377155","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377155","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10479968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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