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ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add TAIL-τ instance-based staleness-adaptive step size (mean-preserving) | AsyncSGD (ASYNC, HOG, LSH) on LeNet · MNIST, Fashion-MNIST, CIFAR-10 | wall-clock time to convergence speedup: up to 1.92x (5%) and avg 1.29-1.6x across settings | Helped |
| Replace constant step size with commonly used ×τ^-1 staleness dampening (ablation) | AsyncSGD on MNIST (MLP and LeNet) · MNIST | convergence rate | Hurt |
| Replace constant step size with FLeet exponential dampening (ablation) | AsyncSGD on MNIST (MLP and LeNet) · MNIST | convergence rate | Hurt |
| Add TAIL-τ step size to AsyncSGD implementations | AsyncSGD (HOG, LSH, ASYNC) · MNIST and Fashion-MNIST higher-precision convergence | fraction of executions reaching target precision: success ratio 3.53 for MLP MNIST 5%-convergence | Helped |
Closest papers by meaning
- Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays (NeurIPS 2022 · similarity 0.91)
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- Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching (ICML 2025 · similarity 0.88)
- Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness (NeurIPS 2024 · similarity 0.88)
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