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

Zhixiong Yang, Arpita Gang, Waheed U. Bajwa

Research output: Contribution to journalArticle

Abstract

Statistical inference and machine-learning algorithms have traditionally been developed for data available at a single location. Unlike this centralized setting, modern data sets are increasingly being distributed across multiple physical entities (sensors, devices, machines, data centers, and so on) for a multitude of reasons that range from storage, memory, and computational constraints to privacy concerns and engineering needs. This has necessitated the development of inference and learning algorithms capable of operating on noncolocated data. For this article, we divide such algorithms into two broad categories, namely, distributed algorithms and decentralized algorithms (see "Is It Distributed or Is It Decentralized?").

Original languageEnglish (US)
Article number9084329
Pages (from-to)146-159
Number of pages14
JournalIEEE Signal Processing Magazine
Volume37
Issue number3
DOIs
StatePublished - May 2020

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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