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71 articles for “federate”
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Federated Learning for Energy Management in Next Generation Smart Cities
Abstract: Federated learning has emerged as a promising approach for addressing the challenges of energy management in next-generation smart cities. This decentralized approach to machine learning allows collaborative model training among distributed data sources, while safeguarding data privacy and security. In this study, we explore the application of federated learning techniques to optimize energy consumption, enhance grid stability, and promote sustainability in smart city environments. By aggregating data from diverse sources …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 1, 2024 · pp. 19–27 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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Evaluation of Parasite Contamination in Residents along Wumba River Water in Federal Capital Territory of Abuja
Abstract: Access to clean drinking water is essential for maintaining a healthy lifestyle, and water quality plays a critical role in human well-being. This study evaluates the prevalence of parasite contamination among residents living along the Wumba River in the Federal Capital Territory of Abuja. Access to clean and safe drinking water is essential for maintaining a healthy lifestyle, and water quality directly impacts various aspects of human well-being. In this …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 13, Issue 2, 2023 · pp. 10–20 Read article
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Comprehensive Security Analysis of Federated Identity Management
Abstract: AbstractAnalyzing the security of FIdM is a challenging task, on one hand due to the various modes and options that the protocols provide, and on the other hand due to the inherent complexity of the web. A thorough understanding of the security vulnerabilities is required to remodel a stable and secure authentication system. In this paper the challenges and requirements of securing the exchange of information between enterprises have been …
Published in Journal of Communication Engineering & Systems · Vol. 7, Issue 1, 2017 · pp. 11–16 Read article
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Federated Learning Framework for Sustainable Multi-Scale Design of Recyclable Thermoplastic Graphene Composites in Smart Manufacturing Environments
Abstract: The growing demand for sustainable advanced materials has accelerated the development of recyclable thermoplastic graphene composites for next-generation smart manufacturing systems. The typical central optimization methods have challenges with data privacy, scalability, and poor collaboration between distributed manufacturing sites. By combining material informatics, edge intelligence and distributed artificial intelligence, this study introduces a Federated Learning (FL) framework to design recyclable thermoplastic graphene composites at multiple scales sustainably. The proposed framework …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and graphics processing unit (GPU)-based architectures without centralizing sensitive data. This work proposes a parallel adaptive federated learning (AFL) framework that integrates differential privacy and secure aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 09–16 Read article
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An Empirical Study Summary on Integrated Building Commissioning for New US Federal Domestic Construction Projects
Abstract: Current management practices for federal domestic construction projects need to be modified to mitigate the negative effects newly constructed buildings have during the first few years of occupation. These negative effects include poor indoor air quality and poor performance of its tenants during the initial years of building occupancy. This empirical study focuses on the staff of the US Mission to the United Nations (USUN) as an off-site temporary facility …
Published in Journal of Construction Engineering, Technology & Management · Vol. 3, Issue 3, 2013 · pp. 37–44 Read article
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Federated Learning for Privacy-Preserving AI Model Training Across Distributed Healthcare Systems
Abstract: Building effective AI diagnostic tools in clinical environments presents a fundamental contradiction — the patient data most critical to model performance is precisely the data subject to the strictest legal and institutional restrictions. Regulations such as HIPAA and GDPR, while essential for protecting patient rights, render conventional centralized training pipelines largely impractical in real hospital settings where data cannot be transferred, pooled, or shared across institutional boundaries. This paper presents …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Federated Learning: A Comprehensive Review of Models, Algorithms, and Business Applications
Abstract: In an age where data privacy is a significant concern, federated learning (FL) has become a game-changing method in machine learning. This decentralized model enables various parties to work together on training models without exchanging their raw data, effectively tackling the issues posed by data silos and privacy regulations. This article explores the current state of FL, including its underlying models and algorithms, practical applications, benefits, challenges, and future directions. …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 1–9 Read article
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Elderly Healthcare Using Federated Learning Approach
Abstract: The healthcare system for elderly people faces several challenges, which can be addressed using advanced machine learning models. These models can help monitor chronic diseases, detect falls, and provide personalized health recommendations. The study uses comprehensive datasets like MIMIC-III/IV, WESAD, and UCIHAR to explore human movements, device limitations, and the differences in fall occurrences. A detailed review of existing literature discusses current technologies for activity monitoring and fall detection, focusing …
Published in Current Trends in Information Technology · Vol. 16, Issue 1, 2025 · pp. 13–23 Read article
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An Adaptive and Privacy-Aware Federated Learning Framework for Efficient and Secure Model Training Across Heterogeneous Datasets
Abstract: The problem of efficiency and privacy regarding heterogeneous data in modern distributed machine learning systems is a vital point that should be taken into account. The absence of IID data distribution, client heterogeneity, and privacy invasion during the aggregation model are the bane of conventional federated learning (FL) approaches to learning like FedAvg and FedProx. The paper proposes that the adaptive and privacy-aware FL framework (AFL-P) can be used to …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 16–25 Read article
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Evaluation of the Security Risks Associated with the Construction and Operations of a Nuclear Power Plant in the Federal Republic of Nigeria
Abstract: AbstractElectricity is paramount for Nigeria’s economic growth. Both, commercialindustrial and domesticactivities mainly rely on electricity for daily needs and smooth operations. Electricity from nuclear energy will further boost the required national power capacity, projected to be above 10,000 MW. This study addresses some associated security problems that may bedevil a Nigerian Nuclear Power Plant Project with the necessary response arrangements if any security breach arises. Paramount among the problems observed …
Published in Journal of Nuclear Engineering & Technology · Vol. 10, Issue 1, 2020 · pp. 15–23 Read article
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The Effect of using Grease on the Surface Roughness of Aluminum 1100 Sheet during the Single Point Incremental Forming Process
Abstract: Incremental forming is a sheet metal forming technique in which a uniform sheet is plastically deformed through the progressive action of a rounded tool. The movement of the tool is governed by a CNC machine. By this way the tool locally deforms the sheet through pure stretching deformation. In this research incremental forming experiments were carried out on Aluminum 1100 sheets to form a cone shape. Roughness was studied by …
Published in Trends in Machine design · Vol. 1, Issue 1, 2014 · pp. 1–4 Read article
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The Significance of an Open and Fair Internet
Abstract: This review work investigates the idea of net neutrality, highlighting its importance, arguing for and against it, and examining its effects on society. According to the concept of "net neutrality", all internet traffic should be treated equally without prejudice or favouring any one data or service over another. The study chronicles the development of net neutrality from its early years, when academic research was the main use of the internet, …
Published in Journal Of Network security · Vol. 11, Issue 1, 2023 · pp. 39–43 Read article
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A Framework for Privacy-preserving AI Models in Cloud Computing: Challenges and Solutions
Abstract: The growing adoption of cloud computing for deploying artificial intelligence (AI) models has led to significant advancements in sectors such as healthcare, finance, and e-commerce. However, the integration of AI with cloud computing raises critical privacy concerns, particularly when handling sensitive data. This paper presents a comprehensive framework for implementing privacy-preserving AI models in cloud environments, addressing the unique challenges, and proposing effective solutions. The suggested framework employs advanced privacy-preserving …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 1–12 Read article
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The Clean Water Act: Legal Analysis and Implementation Challenges
Abstract: The Clean Water Act is one of the most far-reaching environmental laws in the United States; it was enacted with express purposes for the protection and restoration of the country's waters. For many years, though, numerous logistical, administrative, and legal challenges have beset its implementation. This paper will undertake a detailed legal review of the CWA, discussing its foundational principles, major amendments, and evolving interpretations by courts. It does so …
Published in Journal of Water Pollution & Purification Research · Vol. 12, Issue 1, 2025 · pp. 1–9 Read article
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Machine Learning-Based Approach for Heart Disease Prediction
Abstract: Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 64–73 Read article
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A Comprehensive Review on IoT and Edge Computing in Electronics: Trends, Challenges, and Future Directions
Abstract: The Internet of Things (IoT) transformed the electronics industry by enabling ubiquitous connectivity between billions of devices. This has created an unprecedented amount of data, challenging traditional cloud-based architectures with latency, bandwidth, and security issues. Edge computing came as an additive architecture by distributing computation and bringing intelligence to IoT edges to provide real-time responsiveness and reduce dependence on centralized infrastructure. This study offers a thorough analysis of current developments …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 1, 2026 · pp. 1–9 Read article
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Ethical and Responsible AI: A Comprehensive Review of Principles, Methods, and Tools
Abstract: Quick development of artificial intelligence (AI) has revolutionized a number of industries, including healthcare, banking, and government, by providing creative answers to challenging issues. However, there are serious ethical issues with growing integration of AI into crucial decision-making processes, including prejudice, a lack of transparency, abuses of data privacy, and accountability gaps. A systematic strategy that incorporates technical solutions, legal frameworks, and ethical standards is needed to address these issues. …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 23–34 Read article
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A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs)
Abstract: Mobile Ad Hoc Networks (MANETs) comprise wireless networks that lack any conventional infrastructure . Their chief features include highly changing network topologies, lack of centralized administration, and open nature of communication, which collectively result in making MANETs of the wireless kind very susceptible to a diverse range of cyber-attacks like blackhole, greyhole, wormhole, flooding, Sybil and denial-of-service (DoS) among others. Conventionally, Intrusion Detection Systems (IDS) relying on static rule-based methods …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article