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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