Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model

While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorit...

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Vydáno v:	arXiv.org
Hlavní autoři:	Yang, Zhixiong, Gang, Arpita, Bajwa, Waheed U
Médium:	Paper
Jazyk:	angličtina
Vydáno:	Ithaca Cornell University Library, arXiv.org 02.06.2020
Témata:	Algorithms Cybersecurity Decision making Machine learning Robustness Statistical inference Threat models
ISSN:	2331-8422
On-line přístup:	Získat plný text
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