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Manufacturing

Building smart factories in the university lab

August 5, 2026 by Ashish Jagani, Nikunj Rachchh

Smart factories are no longer the exclusive domain of multinational manufacturing corporations with extensive infrastructure and costly automation systems. Engineering students can now design, test, validate, and optimize entire smart factory ecosystems within their laboratories using advanced simulation platforms such as FlexSim and AnyLogic. Free trial versions, academic … [Read more...] about Building smart factories in the university lab

Challenges in simulation of digital twins: A review

May 28, 2025 by Dhanush L. Prakash, Nidhi B. C., Gopalakrishna H. D.

The digitalization of manufacturing processes is on the rise, marking the development of Industry 4.0 and the emergence of smart factories. Digital twins (DTs), which are digital replicas of physical assets or processes, are being utilized to enhance business applications. In the manufacturing sector, DTs can be created for asset-specific production lines or even entire … [Read more...] about Challenges in simulation of digital twins: A review

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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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