A paradigm shift in higher education in the context of the Fourth Industrial Revolution

March 1, 2021 by Jaco du Preez and Saurabh Sinha

The future of work is upon us and has rapidly and profoundly influenced many professions and industries. Alongside this, we argue that it is essential for the landscape of postschool education and training (PSET), both in South Africa and globally, to adapt to the changing nature of work, and, ideally, lead this evolution.

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https://ieeexplore.ieee.org/document/9375034

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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