Novel Gradient Sparsification Algorithm via Bayesian Inference
<|reference_start|>Novel Gradient Sparsification Algorithm via Bayesian Inference: Error accumulation is an essential component of the Top-$k$ sparsification method in distributed gradient descent. It implicitly scales the learning rate and prevents the slow-down of lateral movement, but it can also deteriorate convergence. This paper proposes a novel sparsification algorithm called regularized Top-$k$ (RegTop-$k$) that controls the learning rate scaling of error accumulation. The algorithm is developed by looking at the gradient sparsification as an inference problem and determining a Bayesian optimal sparsification mask via maximum-a-posteriori estimation. It utilizes past aggregated gradients to evaluate posterior statistics, based on which it prioritizes the local gradient entries. Numerical experiments with ResNet-18 on CIFAR-10 show that at $0.1\%$ sparsification, RegTop-$k$ achieves about $8\%$ higher accuracy than standard Top-$k$.<|reference_end|>
@article{bereyhi2024novel,
    title={Novel Gradient Sparsification Algorithm via Bayesian Inference},
    author={Ali Bereyhi and Ben Liang and Gary Boudreau and Ali Afana},
    journal={arXiv preprint arXiv:2409.14893},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14893},
    primaryClass={cs.LG cs.IT eess.SP math.IT}
}