Robust fusion of unreliable data sources using error-correcting output codes

Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemChapter


The emergence of big and dirty data era demands new distributed learning and inference solutions to tackle the problem of inference with corrupted data. The central goal of this chapter is to discuss the presence of corrupted data in the context of distributed inference networks (DINs) and discuss coding-theoretic strategies to ensure reliable inference performance in several practical scenarios. It discusses a generalization of the classical Byzantine Generals problem in the context of distributed inference to different topologies. Over the last three decades, research community has extensively studied the impact of imperfect transmission channels or sensor faults on distributed inference systems. However, corrupted (Byzantine) data models, considered in this chapter, are philosophically different from the imperfect channels or faulty sensor cases. Byzantines are intentional and intelligent and therefore can optimize over the data corruption parameters. While learning their behavior and actively countering them is a viable approach, this chapter presents a new paradigm of mitigation strategies that use coding-theoretic results. The general approach of error-correcting output codes (ECOC) for data fusion is presented and its applicability for several inference problems in practice dealing with unreliable data including Byzantines is shown. This approach is then shown to be applicable to a wider range of inference problems such as classification using crowdsourced data.

Original languageEnglish (US)
Title of host publicationData Fusion in Wireless Sensor Networks
PublisherInstitution of Engineering and Technology
Number of pages21
ISBN (Electronic)9781785615849
StatePublished - Jan 1 2019


  • Chapter
  • Classical byzantine generals problem
  • Codes
  • Coding-theoretic results
  • Coding-theoretic strategies
  • Corrupted data models
  • Crowdsourced data
  • Data corruption parameters
  • Data fusion
  • Data handling techniques
  • Dirty data era demands new distributed learning
  • Distributed inference networks
  • Distributed inference systems
  • Error correction codes
  • Error-correcting output codes
  • Fault tolerant computing
  • General approach
  • Imperfect channels
  • Imperfect transmission channels
  • Inference mechanisms
  • Inference problems
  • Inference solutions
  • Knowledge engineering techniques
  • Learning (artificial intelligence)
  • Other topics in statistics
  • Other topics in statistics
  • Pattern classification
  • Radio links and equipment
  • Reliable inference performance
  • Robust fusion
  • Sensor fusion
  • Unreliable data sources

ASJC Scopus subject areas

  • Engineering(all)
  • Physics and Astronomy(all)
  • Computer Science(all)


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