Surveys

The potential of augmented reality in Indian rural primary education [STEM Education]

July 10, 2024 by Dipali Basumatary, Ranjan Maity

Technology-enhanced learning has the potential to increase educational quality. In this study, we examine the integration of augmented reality (AR) technology in rural primary schools in India. We listed articles associated with AR-based applications for Indian preschool and primary education from 2018 to 2023, and we observed that they are only 10% of contemporary AR-based … [Read more...] about The potential of augmented reality in Indian rural primary education [STEM Education]

Science, technology, engineering, and mathematics at Bule Hora: Experiences, best practices, and outcomes [STEM Education]

July 10, 2024 by Sudhir K. Routray, Sasmita Mohanty

Science, technology, engineering, and mathematics (STEM) outreach programs strive to ignite a passion for STEM among students through interactive sessions, workshops, and mentorship initiatives. These programs, designed to bridge educational gaps and promote inclusivity, engage diverse communities. In Bule Hora, Ethiopia, a STEM outreach project, supported by IEEE and Bule Hora … [Read more...] about Science, technology, engineering, and mathematics at Bule Hora: Experiences, best practices, and outcomes [STEM Education]

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.

Surveys

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