Research map / Federated learning and privacy / Communication-efficient FL
FedBoost: A Communication-Efficient Algorithm for Federated Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| weighted random sampling of base predictors (gamma proportional to alpha*C) instead of uniform sampling (ablation) | FedBoost · synthetic dataset (p=100), C=32 | final loss | Helped |
| weighted random sampling of base predictors instead of uniform sampling (ablation) | FedBoost · Shakespeare (p=715), C=p/2 | loss | Helped |
| communication budget C=64 (sampling all predictors, i.e. C=q) versus sampling subsets (ablation) | FedBoost · synthetic dataset (p=100) | final loss | No clear effect |
| sampling a subset of base predictors per round (C=p/2) versus using all predictors every round (ablation) | FedBoost with weighted sampling · Shakespeare (p=715) | convergence loss | Helped |
Closest papers by meaning
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- Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning (ICML 2024 · similarity 0.88)
- Revisiting Ensembling in One-Shot Federated Learning (NeurIPS 2024 · similarity 0.88)
- Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization (ICLR 2025 · similarity 0.87)
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