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An ethical hangover: A young professional’s case

May 24, 2024 by Andrew Brei, Seppo J. Ovaska

< Engineering ethics is a misunderstood issue among practitioners. Although most electrical and computer engineers agree that general ethical principles should be followed, too few apply or are even familiar with the IEEE Code of Ethics. It is quite rare that the principles of engineering ethics are on the table when practitioners are making decisions and taking … [Read more...] about An ethical hangover: A young professional’s case

Digital transformation, Industry 4.0, and extended reality

July 28, 2023 by Pedro Wightman

Digital transformation has been a very popular topic in the last decade. Every major technology provider and consultancy firm worldwide has its definition for it; however, for many, it has become a buzzword that has been used and abused to sell every possible service or gadget, without helping companies build a clear path for successful adoption that will turn the new … [Read more...] about Digital transformation, Industry 4.0, and extended reality

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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.

POPULAR ARTICLE

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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