A Learning-Based Approach to Approximate Coded Computation
Author:
Affiliation:
1. Technische Universität,Berlin,Germany
2. University of Melbourne,Department of Electrical and Electronic Engineering,Victoria,Australia
3. Fraunhofer Heinrich Hertz Institute,Berlin,Germany
Funder
Ministry of Education
Australian Research Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9965754/9965755/09965865.pdf?arnumber=9965865
Reference23 articles.
1. Successive Approximation Coding for Distributed Matrix Multiplication
2. ϵ-Approximate Coded Matrix Multiplication Is Nearly Twice as Efficient as Exact Multiplication
3. Draco: Byzantine-resilient distributed training via redundant gradients;chen;International Conference on Machine Learning,2018
4. Computation Scheduling for Distributed Machine Learning With Straggling Workers
5. Learning a code: Machine learning for approximate non-linear coded computation;kosaian,2018
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1. Coded Federated Learning for Communication-Efficient Edge Computing: A Survey;IEEE Open Journal of the Communications Society;2024
2. Coded Distributed Image Classification;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17
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