Reimagining transportation with intelligent systems [Editorial]

September 15, 2025 by Zoya Shafique

Intelligent transport systems (ITSs) represent a major shift in how societies move. Their goal is to create transportation networks that adapt intelligently to the world around them by using advanced computing, analytical software and realtime connectivity between vechicles, infrastructure and people. This means fewer delays, safer roads, and better decisions made by travelers who have access to timely, accurate information. ITSs do not just apply to cars on the road but also to ships in ports, train networks, and more.

For more about this article see link below.

https://ieeexplore.ieee.org/document/11429120

For the open access PDF link of this article please click here.

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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