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Stability-Based Generalization Analysis of the Asynchronous Decentralized SGD
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the learning rate (α = 0.1, 0.2, 0.3, or 0.5) (ablation) | AD-SGD · MNIST; CIFAR-100 | generalization error | Hurt |
| Increase maximum asynchronous delay (τ = 16, 32, 64, or 128) (ablation) | AD-SGD · MNIST; CIFAR-100 | generalization error | Helped |
| Increase maximum asynchronous delay (τ = 16, 32, 64, or 128) (ablation) | AD-SGD · CIFAR-100 | training error | Hurt |
| Use a sparser communication topology instead of a more connected topology (ablation) | AD-SGD · MNIST; CIFAR-100 | generalization error | Hurt |
| Increase asynchronous delay (ablation) | AD-SGD · CIFAR-100 | testing error | Helped |
Closest papers by meaning
- Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness (NeurIPS 2024 · similarity 0.94)
- Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness (ICML 2025 · similarity 0.94)
- Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm (ICML 2024 · similarity 0.93)
- Towards Stability and Generalization Bounds in Decentralized Minibatch Stochastic Gradient Descent (AAAI 2024 · similarity 0.93)
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- Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity (NeurIPS 2026 · similarity 0.89)
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