The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
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Independently Published Blurb Createspace Independent Publishing Platform Routledge Lector House World Scientific Publishing Co Pte Ltd Alpha Edition CRC Press Cambridge University Press Kluwer Law International Springer Nature Switzerland AG؛ إصدار 1st ed. 2020 Oxford University Press Book on Demand Ltd. Lulu.com DK Publishing Hardpress Publishing Wentworth Press Coordination Group Publications Ltd Penguin Books Ltd Collins