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Advancing conservation: Internet of Things-driven initiatives for marine animal rescue due to climate changes [Climate Change]

March 6, 2024 by Rahul Sanmugam Gopi, Rajkumar Ramasamy, M. David Honesty Babu

Beneath the expansive oceans lies a complex ecosystem where marine animals, from whales to delicate coral polyps, sustain the vitality of the marine environment. However, these creatures are facing significant challenges due to evolving climate changes. Rising sea temperatures, ocean acidification, habitat degradation, and increasing natural disasters threaten their … [Read more...] about Advancing conservation: Internet of Things-driven initiatives for marine animal rescue due to climate changes [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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