Federated Matrix Factorization with Privacy Guarantee

Summary: Unified framework for federated matrix factorization (vertical, horizontal, local) with provable convergence. Characterizes privacy risks across collection/training/publishing, introduces DP "embedding clipping", and in LFL pairs DP with secure aggregation to approach local-DP privacy with much better utility. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 12960
Venue: VLDB
Year: 2022
Pagerank: 5.3310992e-05
Overall Rank: 5,775 | 59.83%
DOI: 10.14778/3503585.3503598

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,502	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.0361846e-05
6,700	Differentially Private Vertical Federated Clustering	2023	VLDB	4.9563668e-05
8,651	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity	2023	VLDB	4.4757309e-05
9,323	FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data	2023	SIGMOD	4.3556432e-05
10,686	PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning	2025	VLDB	4.1945683e-05
10,978	A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition	2024	SIGMOD	4.1945683e-05

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
83	Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis	2009	SIGMOD	0.00053933811
136	Revealing Information while Preserving Privacy	2003	PODS	0.0004241101
1,143	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00013710269

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