Artificial Intelligence-Driven Intelligent Systems: A Comprehensive Study on Adaptive Learning Models, Bias Mitigation, and Secure Deployment in Large-Scale IoT Environments
Keywords:
Artificial Intelligence, Internet of Things, Adaptive Learning Models, Bias Mitigation, Secure Deployment, Edge Computing, Federated Learning, Trustworthy AIAbstract
Artificial Intelligence (AI)-driven intelligent systems have become foundational in enabling scalable, adaptive, and autonomous decision-making within large-scale Internet of Things (IoT) environments. This paper examines the integration of adaptive learning models, bias mitigation strategies, and secure deployment mechanisms in distributed IoT ecosystems. It explores how machine learning techniques evolve dynamically with streaming data, addresses algorithmic fairness challenges, and outlines robust security architectures for real-world deployment. The study synthesizes existing literature, proposes a conceptual framework, and highlights key challenges and opportunities for future research. Visual representations, including flowcharts and tables, are incorporated to clarify system design and evaluation strategies
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Copyright (c) 2025 Nikolai Pushkin (Author)
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