Sequoia: An Accessible and Extensible Framework for Privacy-Preserving Machine Learning over Distributed Data
Summary: Decouples ML models from secure protocols to simplify PPML on distributed data. Compiler-executor with JAX APIs, extensible PPML policies, and cross-party scheduling enables automatic integration, reducing code by 64-92% and boosting training throughput by 88%. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kaiqiang Xu (Hong Kong University of Science and Technology)
- 2. Di Chai (Hong Kong University of Science and Technology)
- 3. Junxue Zhang (Hong Kong University of Science and Technology)
- 4. Fan Lai (University of Illinois Urbana-Champaign)
- 5. Kai Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{xu_sigmod25,
title = {{Sequoia: An Accessible and Extensible Framework for Privacy-Preserving Machine Learning over Distributed Data}},
author = {Xu, Kaiqiang and Chai, Di and Zhang, Junxue and Lai, Fan and Chen, Kai},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709742},
url = {https://dl.acm.org/doi/10.1145/3709742},
year = {2025}
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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 |
|---|---|---|---|---|
| 1,959 | VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | 2021 | SIGMOD | 9.4090198e-05 |
| 2,473 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 8.5326287e-05 |
| 5,876 | BAGUA: Scaling up Distributed Learning with System Relaxations | 2022 | VLDB | 6.0557672e-05 |
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