IEEE Women in Engineering: Toward a more diverse industry [IEEE Student Activities]

August 5, 2026 by Moira Prates, Ilana Kelly Barboza Lima, Andressa Xavier e Souza Carvalho, Sofia Vale Sacramento, Helen Dandara Lima Ferreira

For many women in engineering, getting into the industrial environment still means facing barriers, stereotypes, and uncertainty about their place in the technical field. This difficulty increases when focusing on leadership positions. The challenges related to diversity and inclusion in advanced stages of technical and leadership careers remain present in many sectors worldwide, especially when it comes to science, technology, engineering, and mathematics (STEM). In response to this reality, volunteers from the IEEE Women in Engineering (WIE) Student Branch (SB) Affinity Group (AG) at the Federal University of Bahia (UFBA) created an initiative designed to connect students and young professionals directly with industry, leadership, and technical development.

For more about this article see link below.

https://ieeexplore.ieee.org/document/11609437

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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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