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LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LAG-WK's adaptive gradient-trigger rule instead of communicating with every worker each iteration (Batch GD) | Distributed linear regression · Linear regression (M=9) | communication complexity: 5283 -> 412 | Helped |
| Use LAG-WK's adaptive gradient-trigger rule instead of communicating with every worker each iteration (Batch GD) | Distributed linear regression · Linear regression (M=18) | communication complexity: 10548 -> 657 | Helped |
| Use LAG-WK's adaptive gradient-trigger rule instead of communicating with every worker each iteration (Batch GD) | Distributed linear regression · Linear regression (M=27) | communication complexity: 15822 -> 1058 | Helped |
| Use LAG-WK's adaptive gradient-trigger rule instead of communicating with every worker each iteration (Batch GD) | Distributed logistic regression · Logistic regression (M=9) | communication complexity: 33309 -> 584 | Helped |
| Use LAG-WK's adaptive gradient-trigger rule instead of communicating with every worker each iteration (Batch GD) | Distributed logistic regression · Logistic regression (M=18) | communication complexity: 65322 -> 1098 | Helped |
| Use LAG-WK's adaptive gradient-trigger rule instead of communicating with every worker each iteration (Batch GD) | Distributed logistic regression · Logistic regression (M=27) | communication complexity: 97821 -> 1723 | Helped |
| Use LAG-WK rather than LAG-PS, which uses a more conservative server-side trigger | LAG · Linear regression (M=9) | communication complexity: 1756 -> 412 | Helped |
| Use LAG-WK rather than LAG-PS, which uses a more conservative server-side trigger | LAG · Logistic regression (M=9) | communication complexity: 14423 -> 584 | Helped |
Closest papers by meaning
- Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients (NeurIPS 2019 · similarity 0.95)
- Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity (NeurIPS 2026 · similarity 0.88)
- Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers (NeurIPS 2025 · similarity 0.88)
- Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates (ICLR 2024 · similarity 0.88)
- HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links (NeurIPS 2024 · similarity 0.87)
- Convergence of Distributed Adaptive Optimization with Local Updates (ICLR 2025 · similarity 0.87)
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