Smart cities

Data communication challenges in smart cities

May 12, 2022 by Anjum Sheikh and Asha Ambhaikar

The localization of jobs as well as educational opportunities in urban areas has resulted in the migration of people toward cities. According to the United Nations Human Settlement program, nearly two-thirds of the current population of Earth will be living in cities by 2050. With this growth, cities will have to deal with issues like efficient transportation, energy … [Read more...] about Data communication challenges in smart cities

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.

Smart cities

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