Secure Networked Inference with Unreliable Data Sources

Description

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. Front Matter ....Pages i-xiii Introduction (Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney)....Pages 1-6 Background (Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney)....Pages 7-16 Distributed Detection with Unreliable Data Sources (Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney)....Pages 17-74 Distributed Estimation and Target Localization (Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney)....Pages 75-135 Some Additional Topics on Distributed Inference (Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney)....Pages 137-150 Distributed Inference with Unreliable Data: Some Unconventional Directions (Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney)....Pages 151-184 Back Matter ....Pages 185-208