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Smart buildings

Accelerating smart campus development with an extensible framework

July 28, 2023 by Bugra Aydin

In recent years, underwater robots have been an essential focus of marine science and technology applications. Whether it is the application of military tasks or general civil affairs, underwater robots have played a significant role. For example, unmanned underwater vehicles (UUVs) can work in the sea for a long time, have high maneuverability, and perform various underwater … [Read more...] about Accelerating smart campus development with an extensible framework

Alexa-based voice assistant for smart home applications

July 1, 2021 by Clara Jiménez, Edgar Saavedra, Guillermo del Campo, and Asunción Santamaría

Today, the Internet of Things (IoT) is becoming an essential player in creating a new smart era. The communication through the Internet to IoT devices enables new applications in multiple environments, including smart buildings and cities. The IoT market is projected to grow to 75.4 billion connected devices by 2020. Within the IoT ecosystem, smart home technology is also … [Read more...] about Alexa-based voice assistant for smart home applications

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About the Magazine

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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