Research map / Optimization / Implicit bias and generalization
Topology-aware Generalization of Decentralized SGD
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Larger spectral gap (better-connected topology, e.g. fully-connected/exponential vs ring) via denser gossip communication | D-SGD consensus model · CIFAR-10, CIFAR-100, Tiny ImageNet (VGG-11, ResNet-18) | validation-minus-training loss difference (generalization gap) | Helped |
| Increasing worker number on a fixed topology (ring, grid, exponential) | D-SGD consensus model · CIFAR-10, CIFAR-100, Tiny ImageNet (VGG-11) with 32 and 64 workers | generalization gap (loss difference) | Hurt |
| Fully-connected topology in the initial training phase versus other topologies | D-SGD consensus model · VGG-11 on CIFAR-10/CIFAR-100/Tiny ImageNet | loss difference in initial training phase | Mixed |
| Stronger gossip/consensus control in initial training phase (keep consensus distance small early) | D-SGD · theoretical analysis (Corollary 5) | upper bound on distributed on-average stability | Helped |
| Generalization bound of D-SGD depends on VGG-11 Hölder constant L (VGG-11 vs ResNet-18 backbone) | D-SGD · CIFAR-10/CIFAR-100/Tiny ImageNet | generalization gap between topologies | No clear effect |
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