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)

Paper ID: 7081
Venue: SIGMOD
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,398 | 27.67%
DOI: 10.1145/3709742

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Rank	Citing Paper	Year	Venue	Pagerank

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,895	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	0.00010180896
2,902	PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel	2023	VLDB	7.93939e-05
5,720	BAGUA: Scaling up Distributed Learning with System Relaxations	2022	VLDB	5.3527734e-05

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