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A Learning-Based Approach to Approximate Coded Computation

arXiv 2205.09818 ↗First posted 19 May 2022cs.IT cs.LG math.ITcited by 0

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Abstract (arXiv)

Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 19 May 2022.

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11 of 24 references.

References · 11

  1. 2018DRACO: Byzantine-resilient Distributed Training via Redundant Gradientscited by 74
  2. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  3. 2017Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplicationcited by 32
  4. 2018On the Optimal Recovery Threshold of Coded Matrix Multiplicationcited by 27
  5. 2018CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplicationcited by 16
  6. 2020Analog Lagrange Coded Computingcited by 13
  7. 2018OverSketch: Approximate Matrix Multiplication for the Cloudcited by 10
  8. 2018Learning a Code: Machine Learning for Approximate Non-Linear Coded Computationcited by 4
  9. 2022Closed-form max-min power control for some cellular and cell-free massive MIMO networkscited by 4
  10. 2019Random Sampling for Distributed Coded Matrix Multiplicationcited by 2
  11. 2022Successive Approximation Coding for Distributed Matrix Multiplicationcited by 2

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