Practical Differentially Private and Byzantine-resilient Federated Learning

Summary: Practical DP-SGD with Byzantine-resilient aggregation for FL; analyzes DP-Byzantine interaction. Leverages DP noise to enhance aggregation; validates theory and experiments, achieving high accuracy under strong DP and up to 90% Byzantine. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6622
Venue: SIGMOD
Year: 2023
Pagerank: 5.0556005e-05
Overall Rank: 6,459 | 55.07%
DOI: 10.1145/3589264

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,459	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	4.5065275e-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 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,895	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	0.00010180896
3,433	LDP-IDS: Local Differential Privacy for Infinite Data Streams	2022	SIGMOD	7.0998035e-05
3,506	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD	7.0291192e-05
5,519	IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy	2022	SIGMOD	5.4619886e-05

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