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Academic Articles & Research Papers
Showing 12 of 12 academic articles
Tongtong Huo, Wei Wu, Xiaoliang Chen, Mingdi Xue et al. • Intelligent Oncology
(2025)
Bone tumors (BTs)—including osteosarcoma, Ewing sarcoma, and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location, histological subtype, and molecular alterations. Re...
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Open Access (DOAJ)
DOI: 10.1016/j.intonc.2025.06.005
Mamoun Alazab, Swarna Priya Ramu, M. Parimala, Praveen Kumar Reddy Maddikunta et al. • IEEE Transactions on Industrial Informatics
(2021)
Federated learning (FL) is a recent development in artificial intelligence, which is typically based on the concept of decentralized data. As cyberattacks are frequently happening in the various applications deployed in real time, most indu...
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DOI: https://doi.org/10.1109/tii.2021.3119038
Mikhail Yurochkin, Mayank Agarwal, Ghosh, Soumya, Kristjan H. Greenewald et al. • arXiv (Cornell University)
(2019)
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1905.12022
Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong • ACM Transactions on Intelligent Systems and Technology
(2019)
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to t...
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Open Access
DOI: https://doi.org/10.1145/3298981
Jiabao Zhang, Jianhua Wang, Yuhong Li, Zhuangzhuang Zhang et al. • Cybersecurity
(2026)
Abstract Backdoor attacks pose a serious threat to Internet-of-Things (IoT) federated learning. In IoT deployments, pronounced non-independent and identically distributed (non-IID) data heterogeneity causes benign client updates to exhibit ...
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Open Access (DOAJ)
DOI: 10.1186/s42400-026-00625-z
Bimal Ghimire, Danda B. Rawat • IEEE Internet of Things Journal
(2022)
Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the emerging Internet of Things (IoT) has gained a lot of attention from the government, academia, and industries in recent years. Federated cybersecurity (F...
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DOI: https://doi.org/10.1109/jiot.2022.3150363
Pijush Kanti Dutta Pramanik, Bijoy Kumar Upadhyaya, Ajay Kushwaha, Debashish Bhowmik • Energy and AI
(2026)
Modern power grids are becoming more distributed and data-intensive, while centralized, cloud-based analytics struggle to keep pace. Routing everything through a central system introduces delays, strains network capacity, complicates compli...
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Open Access (DOAJ)
DOI: 10.1016/j.egyai.2026.100925
Adiah Qazi, Sadiqa Arshad, Ammad Ali Khan Jadoon • Scientific Reports
(2026)
Abstract Enterprise Resource Planning (ERP) systems serve as critical infrastructure for modern organizations, yet their security assessment lacks standardized evaluation frameworks. This study develops and applies a structured Security Mat...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-45550-w
Donald Shenaj, Giulia Rizzoli, Pietro Zanuttigh • IEEE Access
(2023)
Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with hu...
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DOI: https://doi.org/10.1109/access.2023.3310400
Muhammad Hussain • IEEE Access
(2024)
This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural compone...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3378568
Siwei Yu, Jianwei Ma • Reviews of Geophysics
(2021)
Abstract Recently deep learning (DL), as a new data‐driven technique compared to conventional approaches, has attracted increasing attention in geophysical community, resulting in many opportunities and challenges. DL was proven to have the...
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DOI: https://doi.org/10.1029/2021rg000742
Vo Phuc Tinh, Hoang Hai Son, Nguyen Hoang Nam, Duc Ngoc Minh Dang et al. • IEEE Access
(2024)
In the large-scale deployment of federated learning (FL) systems, the heterogeneity of clients, such as mobile phones and Internet of Things (IoT) devices with different configurations, constitutes a significant problem regarding fairness, ...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3475737