Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates
Summary: CELU-VFL uses cache-enabled local updates to cut cross-party communication. Uniform sampling of stale statistics with an instance-weighting scheme tame variance and staleness, preserving sub-linear convergence with up to 6× speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fangcheng Fu (Peking University)
- 2. Xupeng Miao (Peking University)
- 3. Jiawei Jiang (Wuhan University)
- 4. Huanran Xue (Tencent)
- 5. Bin Cui (Peking University)
BibTeX Citation
@article{fu_vldb22,
title = {{Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates}},
author = {Fu, Fangcheng and Miao, Xupeng and Jiang, Jiawei and Xue, Huanran and Cui, Bin},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {10},
pages = {2111--2120},
doi = {10.14778/3547305.3547316},
url = {https://doi.org/10.14778/3547305.3547316},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,197 | Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent | 2023 | VLDB | 5.3794747e-05 |
| 8,812 | FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement | 2023 | SIGMOD | 5.2722828e-05 |
| 10,043 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 5.0921006e-05 |
| 11,324 | PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 503 | Towards a Unified Architecture for in-RDBMS Analytics | 2012 | SIGMOD | 0.00017202276 |
| 1,277 | Privacy Preserving Vertical Federated Learning for Tree-based Models | 2020 | VLDB | 0.00011237888 |
| 2,007 | VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | 2021 | SIGMOD | 9.198944e-05 |
| 2,519 | HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | 2022 | VLDB | 8.3521095e-05 |
| 3,311 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | 2022 | SIGMOD | 7.4392547e-05 |
| 3,872 | SketchML: Accelerating Distributed Machine Learning with Data Sketches | 2018 | SIGMOD | 6.9540368e-05 |
| 4,945 | HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training | 2022 | SIGMOD | 6.3435891e-05 |
| 6,458 | In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle | 2022 | SIGMOD | 5.7773842e-05 |
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