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Communication Efficient Algorithms for Bounding and Approximating the Empirical Entropy in Distributed Systems.

Amit Shahar1, Yuval Alfassi1, Daniel Keren1

  • 1Department of Computer Science, University of Haifa, Haifa 3498838, Israel.

Entropy (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces algorithms to efficiently approximate or bound the empirical entropy of distributed data. These methods reduce communication overhead in large-scale systems by avoiding full data aggregation.

Keywords:
distributed systemsentropyentropy approximationentropy boundssketches

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Area of Science:

  • Information Theory
  • Distributed Systems
  • Data Science

Background:

  • Empirical entropy quantifies data diversity using frequency vectors.
  • Efficient computation, approximation, or bounding of entropy is crucial.
  • Distributed systems require global entropy from local data, posing communication challenges.

Purpose of the Study:

  • To develop algorithms for approximating or bounding the global empirical entropy in distributed systems.
  • To reduce communication overhead associated with aggregating local frequency vectors.

Main Methods:

  • Algorithms designed to estimate global entropy without full data aggregation.
  • Techniques for bounding entropy in distributed environments.

Main Results:

  • Algorithms successfully approximate or bound global empirical entropy.
  • Significant reduction in communication overhead demonstrated.

Conclusions:

  • The developed algorithms provide an efficient solution for estimating data diversity in distributed systems.
  • These methods overcome the communication bottleneck of traditional entropy computation.