The path to sustainability requires more than smart devices [Editorial]

May 9, 2025 by Zoya Shafique

Smart technology encompasses a wide spectrum of innovations aimed at making everyday systems more efficient, reducing unnecessary resource consumption, and supporting long-term sustainability. These technologies rely on sensors, data analytics, automation, and real-time feedback to optimize how we use energy, water, and other essential resources. From homes to entire cities, smart systems can identify inefficiencies that would otherwise go unnoticed and help people make choices that reduce environmental harm.

For more about this article see link below.

https://ieeexplore.ieee.org/document/11339218

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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