Research map / Architectures and efficiency / RNNs and normalization
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
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Closest papers by meaning
- ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training (NeurIPS 2020 · similarity 0.92)
- On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning (AAAI 2020 · similarity 0.89)
- PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization (NeurIPS 2019 · similarity 0.89)
- Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences (NeurIPS 2024 · similarity 0.87)
- Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers (NeurIPS 2025 · similarity 0.87)
- LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression (ICLR 2025 · similarity 0.86)
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