Sparse Gaussian and Expansion-Preserving probabilistic gradient codes achieve BIBD-comparable worst-case robustness while extending feasible system parameters via sparsified random matrices.
Gradient coding: Avoiding stragglers in distributed learning,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.IT 2years
2026 2representative citing papers
A cost-preserving transformation enforces information-theoretic secrecy in distributed computing via null-space augmentation of the allocation matrix and shared randomness injection.
citing papers explorer
-
Probabilistic Gradient Coding via Structure-Preserving Sparsification
Sparse Gaussian and Expansion-Preserving probabilistic gradient codes achieve BIBD-comparable worst-case robustness while extending feasible system parameters via sparsified random matrices.
-
Secure Multi-User Linearly-Separable Distributed Computing
A cost-preserving transformation enforces information-theoretic secrecy in distributed computing via null-space augmentation of the allocation matrix and shared randomness injection.