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Environmental factors

Atmospheric correction: Sentinel for data in remote sensing-based climate change analysis [Climate Change]

March 6, 2024 by Maitrik Shah, Mehul S. Raval, Srikrishnan Divakaran

Climate change is a complex phenomenon influenced by various natural and human-induced factors, contributing to climate changes that affect global and regional weather patterns. Natural events that cause changes in sunlight, volcanoes, and the Earth’s orbit have influenced the climate gradually and have been studied and understood over a long period. Human-induced factors like … [Read more...] about Atmospheric correction: Sentinel for data in remote sensing-based climate change analysis [Climate Change]

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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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