IEEE Potentials Magazine

The magazine for high-tech innovators

Modeling

Privacy-preserving distributed learning: Techniques, applications, and future challenges

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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 … [Read more...] about Privacy-preserving distributed learning: Techniques, applications, and future challenges

Building smart factories in the university lab

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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