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Ensuring Fairness in Federated Learning Services: Innovative Approaches to Client Selection, Scheduling, and Rewards

Ensuring Fairness in Federated Learning Services: Innovative...

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International Conference on Distributed Computing Systems

作者： Meiying Zhang Huan Zhao Sheldon Ebron Ruitao Xie Kan Yang Dept. of Computer Science University of Memphis USA College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Federated Learning (FL) Services enable customers (requesters) to outsource their FL tasks to the FL service provider, who will recruit a group of clients with appropriate datasets to complete the FL task. For a given FL task, how to select appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a new client selection, scheduling, and rewarding scheme to ensure fairness through a three-stage process: 1) multicriteria initial client pool selection, 2) data quality-oriented perround client scheduling, and 3) performance-based rewarding. Specifically, we first define a client selection metric with multiple criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. Furthermore, we formulate the per-round client selection problem into a data quality-oriented scheduling problem that aims to improve model quality and guarantee fairness. We propose a heuristic algorithm to divide the pool into several subsets such that the federated dataset in a subset is close to an independent and identical distribution (iid) while guaranteeing each client is selected at least once in a scheduling period. In addition, we propose a performance-based payment adjustment protocol with a bonus and punishment mechanism, such that the final payment reflects the actual performance of each selected client. Our fairness analysis and experimental results show that our scheme not only can guarantee fairness but also can improve the model quality especially when data are non-iid.

关键词： Measurement Greedy algorithms Analytical models Federated learning Heuristic algorithms Data integrity Data models

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Stratifying large software files to improve prediction performance in software defect prediction 9

Stratifying large software files to improve prediction perfo...

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9th ACIS International Virtual Conference on Applied Computing and Information Technology, ACIT 2022

作者： Alshehri, Yasser Ali Alnazzawi, Noha Hijazi, Haneen Alharbi, Rawan Computer Science and Engineering Dept Yanbu Industrial College Saudi Arabia Dept of Software Engrg Hashemite University Jordan

ISBN: (纸本)9781450397605

Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explored the effect of size by stratifying our sample based on each unit's size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48, and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is better than predicting those in small files. In addition, the results showed higher median values of all performance measures and less variation in each measure. © 2022 ACM.

关键词： Decision trees

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter 3

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Alsmirat, Mohammad Isra University Dept. of Data Science and Artificial Intelligence Amman Jordan Isra University Dept.of Software Engineering Amman Jordan University of Sharjah Dept. of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350382174

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the 'CIC Truth Seeker Dataset 2023' [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention. © 2023 IEEE.

关键词： Sentiment analysis

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SWIN and Vision Transformer-Driven Crack Detection in the Al Qattara Oasis, UAE: Towards Sustainable Infrastructure Management 11

SWIN and Vision Transformer-Driven Crack Detection in the Al...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Ali, Luqman Ambali Parambil, Medha Mohan Swavaf, Muhammed Alnajjar, Fady Aljassmi, Hamad De Man, Adriaan De Emirates Centre for Mobility Research United Arab Emirates University Dept. Computer Science and Software Engineering Al Ain United Arab Emirates United Arab Emirates University Dept. Computer Science and Software Engineering Al Ain United Arab Emirates Emirates Centre for Mobility Research Dept. Civil and Enviromental Engieering Engieering Al Ain United Arab Emirates United Arab Emirates University Dept. Tourism and Heritage Al Ain United Arab Emirates

ISBN: (纸本)9798350367300

Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pressing need to preserve and maintain built heritage by employing modern technological solutions for infrastructure upkeep. Specifically, it focuses on crack detection, a critical aspect of ensuring the structural integrity of heritage buildings. Utilizing data collected from various structures across the UAE, obtained through handheld cameras, the effectiveness of Vision Transformers (ViTs) and Swin Transformers in identifying cracks within Al Qattara is assessed. The research thoroughly evaluates models, with particular attention to different input patch sizes. Through systematic experimentation and analysis over 100 epochs, ViT models with a patch size 16 exhibit significant promise in crack detection. Notably, the ViT-16 model achieves good performance metrics, including training and validation accuracies of 85% and (82%) respectively, and correctly classifies 791 out of 957 cracks and 772 out of 903 non-cracks. In contrast, Swin Transformers show lower validation accuracies (70-74%) and higher misclassification rates. The outcomes underscore the potential of ViT models in enhancing infrastructure maintenance efforts within heritage sites like Qattara Oasis. By combining advanced technology with a profound respect for historical preservation, this study aims to contribute to the sustainable conservation and protection of cultural heritage, ensuring that future generations can continue to appreciate the enduring legacy of sites such as Qattara Oasis. © 2024 IEEE.

关键词： Historic preservation

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Handcrafted and Deep features based Classification of Scoliosis 2

Handcrafted and Deep features based Classification of Scolio...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Fatima, Joddat Mohsan, Mashood M. Qaisar, Muhammad Umair Hamza, Muhammad Tahir, Muhammad Zeshan Zaman, Ghazzal Bahria University Dept. of Computer Science Islamabad Pakistan National University of Sciences & Technology Dept. of Computer & Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9781665498197

The Spinal cord acts as the central transmission line connecting the Brain with all other body organs. Vertebrae are 33 uneven bones stacked over one another that holds the whole skeleton structure. Scoliosis is the three-dimensional spinal deformity which commonly occurs during the growing age and erupts before puberty. It is further classified in two Shapes C and S. Our research work has two stages, in first stage we segment out the vertebral column using Mask-RCNN. The segmented column is used for features extraction and in stage two feature based classification is done for normal, C and S shape of scoliosis using AASCE2019 dataset. A comparative study on multiple image classification networks is also conducted and based on results EfficientNet-B4 is selected for formulation of hybrid feature set. The accuracy achieved using Random forest classifier, for handcrafted and deep features was up to 94.32% and 89.66%. Hybrid feature set formulated with combination of deep and handcrafted features attained accuracy up to 94.45%. © 2022 IEEE.

关键词： Decision trees

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RNN-based Model for an Optimal COVID-19 Cases Detection using Clinical Reports 9

RNN-based Model for an Optimal COVID-19 Cases Detection usin...

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9th International Conference on Optimization and Applications, ICOA 2023

作者： Khanday, Akib Bouktif, Salah Ouni, Ali Uae University Cit Dept. Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec Ets Montreal MontrealQC Canada

ISBN: (纸本)9798350312546

The global impact of COVID-19 has been significant due to its rapid spread and high transmission rate. A large number of individuals have been exposed to this dangerous virus, emphasizing the importance of early detection to potentially save numerous lives. This research paper introduces an enhanced method for COVID-19 detection, utilizing a Recurrent Neural Network (RNN) and leveraging early clinical reports. Specifically, a Long Short Term Memory (LSTM) model is trained using data obtained from the metadata of the available dataset published by the World Health organisation, specifically focusing on extracting clinical reports and labels. Several pre-processing techniques and word embedding are applied to train an LSTM-based classifier that accurately identifies positive COVID-19 cases. The implementation of this proposed approach yielded superior results compared to traditional machine learning algorithms. The model achieved a testing accuracy of 87%, and future works include expanding the dataset to enhance the efficiency of the model. © 2023 IEEE.

关键词： COVID-19

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Measurement of Ocular Torsion Using Feature Matching on the Iris and Sclera

Measurement of Ocular Torsion Using Feature Matching on the ...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Arthur Bell Richard Green Aluna Everitt Dept. of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

The tracking of torsional eye movements, where the eye rotates about the axis of vision, is important for the diagnosis of certain vestibular disorders, but presents additional challenges when compared to the tracking of linear eye movements. This paper proposes a method for the measurement of ocular torsion that combines techniques from several previous studies, attempting to make the measurement robust to a highly off-axis centre of vision and obscurations of the pupil and iris. The proposed method detects feature points across a region of interest (ROI) including both the iris and the sclera (the white part of the eye surrounding the iris), using the Affine-SIFT (ASIFT) feature detection method, and tracks the feature points across frames using the Lucas-Kanade Optical Flow Method. The proposed method was only able to achieve an average error of 0.42 degrees compared to 0.17 degrees achieved in prior research. However, an additional 23.5% of feature points were able to be tracked compared to feature matching on the iris alone, showing that the proposed method has the potential to provide more accurate tracking than conventional methods. Future improvements proposed to the method include a deep segmentation method to automatically determine the ROI in each frame, and further testing with a calibration step to determine the source of error in the method.

关键词： Iris Image segmentation Accuracy Tracking Feature detection Feature extraction Calibration Pupils Optical flow Testing

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