Collaborative Decoding of Polynomial Codes for Distributed Computation
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Abstract (arXiv)
We show that polynomial codes (and some related codes) used for distributed matrix multiplication are interleaved Reed-Solomon codes and, hence, can be collaboratively decoded. We consider a fault tolerant setup where t worker nodes return erroneous values. For an additive random Gaussian error model, we show that for all t < N-K, errors can be corrected with probability 1. Further, numerical results show that in the presence of additive errors, when L Reed-Solomon codes are collaboratively decoded, the numerical stability in recovering the error locator polynomial improves with increasing L.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2019.
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Citing papers per year: 2021: 3.
3 papers.
- 2021ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systemscited by 5
- 2021List-Decodable Coded Computing: Breaking the Adversarial Toleration Barriercited by 4
- 2021Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learningcited by 4
- 2021ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems20 Sep 2021
- 2021Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning27 Jul 2021
- 2021List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier27 Jan 2021