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Effectiveness of an Internet of Things-based smart farming workshop model for enhancing science, technology, mathematics, and engineering awareness and seeding

May 5, 2025 by Siva Priya Thiagarajah, Yew-Chiong Lo, Fabian Wai Lee Kung, Mohamad Yusoff Alias, Azwan Mahmud, Muhammad Asif Shaik, Tung Tze Yang, Lim Zing

Science, technology, engineering, and mathematics (STEM) education plays a fundamental role in achieving the 17 United Nations Sustainable Development Goals. STEM education awareness can be enhanced through engaging workshops that allow students to appreciate how the Internet of Things (IoT) can be applied to increase convenience and productivity in daily tasks and in industry. … [Read more...] about Effectiveness of an Internet of Things-based smart farming workshop model for enhancing science, technology, mathematics, and engineering awareness and seeding

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IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

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Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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