| 1. |
- Banerjee, Sourasekhar, et al.
(author)
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Cost-efficient feature selection for horizontal federated learning
- 2024
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In: IEEE Transactions on Artificial Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 2691-4581.
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Journal article (peer-reviewed)abstract
- Horizontal Federated Learning exhibits substantial similarities in feature space across distinct clients. However, not all features contribute significantly to the training of the global model. Moreover, the curse of dimensionality delays the training. Therefore, reducing irrelevant and redundant features from the feature space makes training faster and inexpensive. This work aims to identify the common feature subset from the clients in federated settings. We introduce a hybrid approach called Fed-MOFS 1 , utilizing Mutual Information and Clustering for local feature selection at each client. Unlike the Fed-FiS, which uses a scoring function for global feature ranking, Fed-MOFS employs multi-objective optimization to prioritize features based on their higher relevance and lower redundancy. This paper compares the performance of Fed-MOFS 2 with conventional and federated feature selection methods. Moreover, we tested the scalability, stability, and efficacy of both Fed-FiS and Fed-MOFS across diverse datasets. We also assessed how feature selection influenced model convergence and explored its impact in scenarios with data heterogeneity. Our results show that Fed-MOFS enhances global model performance with a 50% reduction in feature space and is at least twice as fast as the FSHFL method. The computational complexity for both approaches is O( d 2 ), which is lower than the state-of-the-art.
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| 2. |
- Sarmah, Roshmi, et al.
(author)
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SURE-H : A Secure IoT Enabled Smart Home System
- 2019
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In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). - : IEEE. - 9781538649800 ; , s. 59-63
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Conference paper (peer-reviewed)abstract
- With the growing technology, the demand for smart things is drastically increased in daily-life. The IoT (Internet of Things) is one of the major components that provides facility to interact with IoT enabled devices. In this work, we propose a secure and efficient smart home system that enable to protect homes from theft or unusual activities and parallelly saves power. Our system is developed by exploiting the features of IoT that facilitates us to monitor an IoT enabled home from anywhere anytime over the Internet when data are stored in the cloud. This system uses a motion detector to detect a moving object from the environment where the system is deployed. The proposed system is evaluated using real-time deployment at KU campus considering 30 rooms for 60 days and found really useful in terms of safeness from any theft and saving power in comparison to existing systems.
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| 3. |
- Agarwal, Ayush, et al.
(author)
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Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach Using Under-Sampling and Class Weighting
- 2020
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In: ICONIP 2020: Neural Information Processing. - Thailand : Springer Berlin/Heidelberg. - 9783030638221 - 9783030638238 ; , s. 113-120
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Conference paper (peer-reviewed)abstract
- With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN) based approach for the identification and classification of cyberbullying posts. In highly imbalanced input data, a Tomek Links approach does under-sampling to reduce the data imbalance and remove ambiguities in class labelling. Further, the proposed classification model uses Max-Pooling in combination with Bi-directional Long Short-Term Memory (LSTM) network and attention layers. The proposed model is evaluated using Wikipedia datasets to establish the effectiveness of identifying and classifying cyberbullying posts. The extensive experimental results show that our approach performs well in comparison to competing approaches in terms of precision, recall, with F1 score as 0.89, 0.86 and 0.88, respectively.
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| 4. |
- Alnefaie, Ahlam, et al.
(author)
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End-to-End Analysis for Text Detection and Recognition in Natural Scene Images
- 2020
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In: 2020 International Joint Conference on Neural Networks (IJCNN). - : IEEE Computer Society. - 9781728169279 - 9781728169262
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Conference paper (peer-reviewed)abstract
- Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.
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| 5. |
- Babou, Cheikh Saliou Mbacke, et al.
(author)
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Hierarchical Load Balancing and Clustering Technique for Home Edge Computing
- 2020
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In: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 127593-127607
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Journal article (peer-reviewed)abstract
- The edge computing system attracts much more attention and is expected to satisfy ultra-low response time required by emerging IoT applications. Nevertheless, as there were problems on latency such as the emerging traffic requiring very sensitive delay, a new Edge Computing system architecture, namely Home Edge Computing (HEC) supporting these real-time applications has been proposed. HEC is a three-layer architecture made up of HEC servers, which are very close to users, Multi-access Edge Computing (MEC) servers and the central cloud. This paper proposes a solution to solve the problems of latency on HEC servers caused by their limited resources. The increase in the traffic rate creates a long queue on these servers, i.e., a raise in the processing time (delay) for requests. By leveraging, based on clustering and load balancing techniques, we propose a new technique called HEC-Clustering Balance. It allows us to distribute the requests hierarchically on the HEC clusters and another focus of the architecture to avoid congestion on a HEC server to reduce the latency. The results show that HEC-Clustering Balance is more efficient than baseline clustering and load balancing techniques. Thus, compared to the HEC architecture, we reduce the processing time on the HEC servers to 19% and 73% respectively on two experimental scenarios.
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| 6. |
- Banerjee, Sourasekhar, et al.
(author)
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Fed-FiS: A Novel Information-Theoretic Federated Feature Selection for Learning Stability
- 2021
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In: Neural Information Processing. - Cham : Springer Nature. - 9783030923068 - 9783030923075 ; , s. 480-487
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Conference paper (peer-reviewed)abstract
- In the era of big data and federated learning, traditional feature selection methods show unacceptable performance for handling heterogeneity when deployed in federated environments. We propose Fed-FiS, an information-theoretic federated feature selection approach to overcome the problem occur due to heterogeneity. Fed-FiS estimates feature-feature mutual information (FFMI) and feature-class mutual information (FCMI) to generate a local feature subset in each user device. Based on federated values across features and classes obtained from each device, the central server ranks each feature and generates a global dominant feature subset. We show that our approach can find stable features subset collaboratively from all local devices. Extensive experiments based on multiple benchmark iid (independent and identically distributed) and non-iid datasets demonstrate that Fed-FiS significantly improves overall performance in comparison to the state-of-the-art methods. This is the first work on feature selection in a federated learning system to the best of our knowledge.
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| 7. |
- Banerjee, Sourasekhar, et al.
(author)
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Multi-Diseases Classification From Chest-X-Ray: A Federated Deep Learning Approach
- 2020
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In: AI 2020: Advances in Artificial Intelligence. - Cham : Springer. - 9783030649838 - 9783030649845 ; , s. 3-15
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Conference paper (peer-reviewed)abstract
- Data plays a vital role in deep learning model training. In large-scale medical image analysis, data privacy and ownership make data gathering challenging in a centralized location. Hence, federated learning has been shown as successful in alleviating both problems for the last few years. In this work, we have proposed multi-diseases classification from chest-X-ray using Federated Deep Learning (FDL). The FDL approach detects pneumonia from chest-X-ray and also identifies viral and bacterial pneumonia. Without submitting the chest-X-ray images to a central server, clients train the local models with limited private data at the edge server and send them to the central server for global aggregation. We have used four pre-trained models such as ResNet18, ResNet50, DenseNet121, and MobileNetV2, and applied transfer learning on them at each edge server. The learned models in the federated setting have compared with centrally trained deep learning models. It has been observed that the models trained using the ResNet18 in a federated environment produce accuracy up to 98.3%98.3% for pneumonia detection and up to 87.3% accuracy for viral and bacterial pneumonia detection. We have compared the performance of adaptive learning rate based optimizers such as Adam and Adamax with Momentum based Stochastic Gradient Descent (SGD) and found out that Momentum SGD yields better results than others. Lastly, for visualization, we have used Class Activation Mapping (CAM) approaches such as Grad-CAM, Grad-CAM++, and Score-CAM to identify pneumonia affected regions in a chest-X-ray.
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| 8. |
- Banerjee, Sourasekhar, et al.
(author)
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Optimized and adaptive federated learning for straggler-resilient device selection
- 2022
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In: 2022 International Joint Conference on Neural Networks (IJCNN). - : IEEE. ; , s. 1-9
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Conference paper (peer-reviewed)abstract
- Federated Learning (FL) has evolved as a promising distributed learning paradigm in which data samples are disseminated over massively connected devices in an IID (Identical and Independent Distribution) or non-IID manner. FL follows a collaborative training approach where each device uses local training data to train local models, and the server generates a global model by combining the local model's parameters. However, FL is vulnerable to system heterogeneity when local devices have varying computational, storage, and communication capabilities over time. The presence of stragglers or low-performing devices in the learning process severely impacts the scalability of FL algorithms and significantly delays convergence. To mitigate this problem, we propose Fed-MOODS, a Multi-Objective Optimization-based Device Selection approach to reduce the effect of stragglers in the FL process. The primary criteria for optimization are to maximize: (i) the availability of the processing capacity of each device, (ii) the availability of the memory in devices, and (iii) the bandwidth capacity of the participating devices. The multi-objective optimization prioritizes devices from fast to slow. The approach involves faster devices in early global rounds and gradually incorporating slower devices from the Pareto fronts to improve the model's accuracy. The overall training time of Fed-MOODS is 1.8× and 1.48× faster than the baseline model (FedAvg) with random device selection for MNIST and FMNIST non-IID data, respectively. Fed-MOODS is extensively evaluated under multiple experimental settings, and the results show that Fed-MOODS has significantly improved model's convergence and performance. Fed-MOODS maintains fairness in the prioritized participation of devices and the model for both IID and non-IID settings.
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| 9. |
- Banerjee, Sourasekhar, et al.
(author)
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Personalized multi-tier federated learning
- 2022
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Conference paper (peer-reviewed)abstract
- The challenge of personalized federated learning (pFL) is to capture the heterogeneity properties of data with in-expensive communications and achieving customized performance for devices. To address that challenge, we introduced personalized multi-tier federated learning using Moreau envelopes (pFedMT) when there are known cluster structures within devices. Moreau envelopes are used as the devices’ and teams’ regularized loss functions. Empirically, we verify that the personalized model performs better than vanilla FedAvg, per-FedAvg, and pFedMe. pFedMT achieves 98.30% and 99.71% accuracy on MNIST dataset under convex and non-convex settings, respectively.
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| 10. |
- Banerjee, Sourasekhar, et al.
(author)
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Towards post-disaster damage assessment using deep transfer learning and GAN-based data augmentation
- 2023
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In: ICDCN '23. - New York, NY, USA : ACM Digital Library. - 9781450397964 ; , s. 372-377
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Conference paper (peer-reviewed)abstract
- Cyber-physical disaster systems (CPDS) are a new cyber-physical application that collects physical realm measurements from IoT devices and sends them to the edge for damage severity analysis of impacted sites in the aftermath of a large-scale disaster. However, the lack of effective machine learning paradigms and the data and device heterogeneity of edge devices pose significant challenges in disaster damage assessment (DDA). To address these issues, we propose a generative adversarial network (GAN) and a lightweight, deep transfer learning-enabled, fine-tuned machine learning pipeline to reduce overall sensing error and improve the model's performance. In this paper, we applied several combinations of GANs (i.e., DCGAN, DiscoGAN, ProGAN, and Cycle-GAN) to generate fake images of the disaster. After that, three pre-trained models: VGG19, ResNet18, and DenseNet121, with deep transfer learning, are applied to classify the images of the disaster. We observed that the ResNet18 is the most pertinent model to achieve a test accuracy of 88.81%. With the experiments on real-world DDA applications, we have visualized the damage severity of disaster-impacted sites using different types of Class Activation Mapping (CAM) techniques, namely Grad-CAM++, Guided Grad-Cam, & Score-CAM. Finally, using k-means clustering, we have obtained the scatter plots to measure the damage severity into no damage, mild damage, and severe damage categories in the generated heat maps.
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