Research map / Optimization / Distributed and compressed
Robust and Communication-Efficient Collaborative Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use deadline-based gradient computation and quantized message passing instead of fixed-batch, unquantized DSGD | Decentralized neural-network training · CIFAR-10 | runtime speedup vs DSGD: 3 × | Helped |
| Add deadline-based gradient computation to quantized decentralized SGD | Q-DSGD · CIFAR-10 | runtime speedup vs Q-DSGD: 2 × | Helped |
| Use deadline-based computation and quantized message passing rather than asynchronous updates | Asynchronous DSGD · CIFAR-10 | runtime improvement vs Asynchronous DSGD: up to 30 % | Helped |
| Use deadline-based gradient computation and quantized message passing | Decentralized neural-network training · CIFAR-10, four-hidden-layer network | runtime speedup vs best of DSGD and Q-DSGD: 2.31 × | Helped |
| Use deadline-based gradient computation and quantized message passing | Decentralized neural-network training · ImageNet, one-hidden-layer network | runtime speedup vs best of DSGD and Q-DSGD: 1.7 × | Helped |
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