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A primer on blockchain and queueing theory: Understanding transaction flows through waiting lines

August 5, 2026 by Shamal Lalvani, Aggelos Katsaggelos

Blockchains are decentralized systems that allow for transactions among parties to be securely processed without the need for a central intermediary, such as a bank in the case of currency exchange. Since the inception of Bitcoin (the first implementation of blockchain) in 2009, considerable advancements have been made in the field of blockchain, spanning applications to … [Read more...] about A primer on blockchain and queueing theory: Understanding transaction flows through waiting lines

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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