FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Summary: FederatedScope: event-driven FL platform to declaratively describe heterogeneous participant behaviors—local training, objectives, backends—and orchestrate synchronous or asynchronous training. Extensible plugin ecosystem (privacy, attack simulation, auto-tuning) and open-source release for research and industrial use. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuexiang Xie (Alibaba)
- 2. Zhen Wang (Alibaba)
- 3. Dawei Gao (Alibaba)
- 4. Daoyuan Chen (Alibaba)
- 5. Liuyi Yao (Alibaba)
- 6. Weirui Kuang (Alibaba)
- 7. Yaliang Li (Alibaba)
- 8. Bolin Ding (Alibaba)
- 9. Jingren Zhou (Alibaba)
BibTeX Citation
@article{xie_vldb23,
title = {{FederatedScope: A Flexible Federated Learning Platform for Heterogeneity}},
author = {Xie, Yuexiang and Wang, Zhen and Gao, Dawei and Chen, Daoyuan and Yao, Liuyi and Kuang, Weirui and Li, Yaliang and Ding, Bolin and Zhou, Jingren},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {5},
pages = {1059--1072},
doi = {10.14778/3579075.3579081},
url = {https://doi.org/10.14778/3579075.3579081},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,257 | FS-REAL: A Real-World Cross-Device Federated Learning Platform | 2023 | VLDB | 5.4574671e-05 |
| 8,456 | Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs | 2024 | VLDB | 5.4217837e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 775 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00014110531 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 5,588 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB | 6.1564686e-05 |
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