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FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler 41

FedCal: Achieving Local and Global Calibration in Federated ...

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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Hongyi Yu, Han Tang, Xiaoli Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average. Copyright 2024 by the author(s)

关键词： Federated learning

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Heart Disease Prediction using Machine Learning 3

Heart Disease Prediction using Machine Learning

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Telsang, Supriya Pokale, Tanvi Posugade, Anagha Patwal, Pradyot Shirode, Prachi Popade, Om Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Information Technology Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350364569

Heart attack and other heart diseases have acted as the major global cause of mortality. In accordance with WHO, about 32% death was due to cardiovascular disease, among which 85% was only due to heart disease There are various factors that lead to heart disease including high LDL, cholesterol, high blood pressure, Diabetes Smoking, Unhealthy diet, Obesity and Physical inactivity. If heart disease is not predicted earlier, it reduces patient's chances of survival. Therefore, it is essential to design a secure and precise system to predict heart disease early for proper treatment of patients. This research paper presents comprehensive analysis to identify heart disease using different predictive analytic methods. The research utilized a dataset of 1025 instances with 13 input parameters compromising age, sex, chest pain, blood pressure, blood sugar, electrocardiographic results, heart rate etc. The research tests and implements various predictive analytic methods such as Decision tree, Random Forest, Logistic regression and KN neighbor to predict the risk of heart disease on 60:40, 70:30 and 80:20 ratio data split. This research paper also highlights the superiority of Random Forest model for predicting heart disease with highest accuracy. © 2024 IEEE.

关键词： decision tree Heart disease KNN Logistic regression Randon Forest

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Bidirectional Attention Mechanism-Based Deep Learning Model for Text Classification Under Natural Language Processing

Bidirectional Attention Mechanism-Based Deep Learning Model ...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Pande, Sagar Dhanraj Kumaresan, T. Lanke, Govinda Rajulu Degadwala, Sheshang Dhiman, Gaurav Soni, Mukesh School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Engineering Sigma Institute of Engineering Gujarat Vadodara India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of CSE University Centre for Research and Development Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9789819931767

Existing text classification models based on graph convolutional networks usually update node representations simply by fusing neighborhood information of different orders through adjacency matrices, resulting in an insufficient representation of node semantic information. In addition, models based on conventional attention mechanisms are only Word vectors which are forward-weighted representations, ignoring the impact of negative words on the final classification. This paper proposes a model based on a bidirectional attention mechanism and a gated graph convolutional network to solve the above problems. The model first uses the gated graph convolutional network, selectively fuses the multi-order neighbourhood information of the nodes in the graph, retains the information of the previous order, and enriches the feature representation of the nodes. Secondly, the influence of different words on the classification results is learned through a two-way attention mechanism. While giving positive weights to terms that play a positive role in classification, negative consequences are given to words that have adverse effects of weakening their influence in vector representation, thereby improving the model's ability to discriminate nodes with different properties in documents. Finally, maximum pooling and average pooling are used to fuse the vector representation of the word, and the document representation is obtained for the final classification. Experiments are carried out on four benchmark data sets, and the results show that the method is significantly better than the baseline model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Detection of Behavioral Patterns of Viral Hepatitis Using data Mining 4

Detection of Behavioral Patterns of Viral Hepatitis Using Da...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Shende, Vaishali Gajendra Jency, A. Pavithra, K. Jayasudha, T. Venkatesan, G. Sharma, Manish East Point College of Engineering and Technology Department of Computer Science Engineering Bangalore560049 India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Thiruvallur601206 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai600123 India Kpr Institute of Engineering and Technology Department of English Coimbatore641407 India Saveetha Engineering College Department of Civil Engineering Chennai602105 India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun248002 India

ISBN: (纸本)9798350300888

The data collected by the Information Systems related to Public Health are underutilized, mainly due to their large volume and their complexity. This work was applied to the Knowledge Discovery in databases technique based on the National Centre for Disease Control (NCDC), with the objective of tracing the epidemiological profile of viral hepatitis. Foram used the data referring to the corporation of Chennai in the year 2022, from two quais it was possible to compare the information obtained with the knowledge available in the specialized literature. Obtain a clear, simple and objective representation of the epidemiological profile of viral hepatitis through a decision tree and classification rules. © 2023 IEEE.

关键词： Decision trees

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Autoencoder based Temporal Convolutional Network for Anomaly Detection in data Streams 1

Autoencoder based Temporal Convolutional Network for Anomaly...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Tamilselvan, K. Riadhwsein, Ramee Sundaram, S. Meenakshi Vigneshwaran, K.S. Abhinaya, S. Avs Engineering College Department of Information Technology Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331529246

Nowadays, the rapid growth of data stream from different sources has needed the development of effective Anomaly Detection (AD) methods. The existing unsupervised Deep Learning (DL) approaches learnt various patterns and relationships in data streams without labeled data. However, detecting anomalies in data streams led to significant challenges such as handling high-dimensional data and capturing temporal dependencies. Hence, this research proposes an Autoencoder based Temporal Convolutional Network (AE-TCN) for AD in data streams. Initially, the input data is collected from Numenta Anomaly Benchmark (NAB) repository and further preprocessed with Regression Imputation (RI) to handle missing values and logarithmic scaling to normalize the data respectively. After that, Robustly Optimized Bidirectional Encoder Representation from Transformer approach (RoBERTa) is employed to extract the semantic features. Finally, AE-TCN is introduced to identify the anomalies in data streams by extracting temporal features from reconstructed nominal patterns. From the results, the proposed AE-TCN established high results in terms of Accuracy (Acc), F1-score, and Area Under Curve (AUC) of 99.89%, 94.06%, and 99.85% respectively when compared with existing Deep Autoencoder Neural Network with Adamax Optimization (DANN-AO). © 2025 IEEE.

关键词： Spatio-temporal data

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Design and Analysis of Deep Learning based Water Potability Prediction

Design and Analysis of Deep Learning based Water Potability ...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Arumugam, M. Vijayakumar, P. Kannan, A.C. Velmurugan, J. Arun, G. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - Pg Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi638057 India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300857

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples. © 2023 IEEE.

关键词： Potable water

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Wireless Sensing with Movable Antennas: Performance and Optimization

Wireless Sensing with Movable Antennas: Performance and Opti...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ma, Wenyan Zhu, Lipeng Zhang, Rui The Chinese University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

ISBN: (纸本)9798350351255

In this paper, we propose a new wireless sensing system equipped with the movable-antenna (MA) array for improving the sensing performance. First, we show that the angle estimation performance in wireless sensing is fundamentally determined by the array geometry, where the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle of arrival (AoA) estimation is derived as a function of the MAs' positions. Then, we aim to achieve the minimum of maximum (min-max) CRBs of estimation MSE for the two AoAs with respect to the horizontal and vertical axes, respectively. In particular, for the special case of circular antenna movement region, an optimal solution for the MAs' positions is derived under certain numbers of MAs and circle radii. Thereby, both the lower- and upper-bounds of the min-max CRB are obtained for the antenna movement region with arbitrary shapes. Moreover, we develop an efficient alternating optimization algorithm to obtain a locally optimal solution for MAs' positions by iteratively optimizing one between their horizontal and vertical coordinates with the other being fixed. Numerical results demonstrate that our proposed MA arrays can significantly decrease the CRB of AoA estimation MSE as well as the actual MSE compared to conventional uniform planar arrays (UPAs) with different values of uniform inter-antenna spacing. © 2024 IEEE.

关键词： Mean square error

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Dynamic Resource Management with Graphic Deep Reinforcement Learning in Space-Air-Ground Integrated Networks

Dynamic Resource Management with Graphic Deep Reinforcement ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cai, Yue Cheng, Peng Chen, Zhuo Xiang, Wei Vucetic, Branka Li, Yonghui The University of Sydney School of Electrical and Information Engineering Australia La Trobe University Department of Computer Science and Information Technology Australia Csiro Data61 Australia

ISBN: (纸本)9798350351255

Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art deep reinforcement learning (DRL) approaches by achieving the highest reward and lowest overall latency. © 2024 IEEE.

关键词： Quality of service

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms 8

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Suguna, R. Deepa, S. Kanimozhi, T. Chandru, M. Arun, G. Harshini, M. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamilnadu Erode638401 India Kongu Engineering College Perundurai Department of Computer Technology - Pg Tamilnadu Erode638060 India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641202 India College Department of Computer Science Erode India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Kongu Engineering College Tamilnadu Perundurai Erode638060 India

ISBN: (纸本)9798350367904

Agriculture is the backbone of India's economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity. © 2024 IEEE.

关键词： Crops

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Evaluation of Deep Learning Algorithms for Instance Segmentation on Cassava Root 6

Evaluation of Deep Learning Algorithms for Instance Segmenta...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Tanasaksakul, Chanowat Akkarajitsakul, Khajonpong Wojciechowski, Tobias King Mongkut's University of Technology Thonburi Department of Computer Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Big Data Experience Center Department of Computer Engineering Bangkok Thailand Jülich Germany

ISBN: (纸本)9798350323535

Different algorithms were developed and evaluated for segmenting images of cassava roots. Cassava CSRs (CSR) have different shapes and sizes. Existing methods rely on image processing algorithms to segment CSR, but these often have problems with overlapping fibrous roots. In this study, deep learning architectures for instance segmentation HTC, SOLOv2, and Mask R-CNN were applied to CSR data to investigate whether these models can overcome the specific challenges of this domain. We created a custom dataset with 1,227 training, 100 validation, and 100 test images of CSRs with different root systems and high-quality segmentation masks. Then, we compared the result of the SOLOv2 model to the Mask R-CNN and HTC models using the same network architecture. The standard metric for comparing instance segmentation models in computer vision such as mean average precision, was used for the evaluation. The results showed that all tested models applied to the considered area showed good prediction accuracy. SOLOv2 showed the best results on the test set with the computation of the model less than that of HTC. Increasing the model complexity and size did not impact the prediction accuracy in the instance segmentation of CSR. HTC achieved almost the same result as that of the SOLOv2 model but required almost doubling the number of parameters. The visualization of the predictions showed quality differences in terms of the accuracy of the segmentation masks. The SOLOv2 model showed the best masks overall. However, each model had problems in assigning the segmentation masks due to the overlap of a CSR. Many root systems still need to be measured for gene discovery and subsequent breeding. The results demonstrated the potential of deep learning instance segmentation models for CSRs needs to be increased with further research. © 2023 IEEE.

关键词： Object detection

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