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Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation

Summary: Unified empirical evaluation of federated-learning contribution estimation methods across effectiveness (coalition-aware utility), robustness to attacks (replication, label-flip), and computational cost. Surveys prior methods, reveals trade-offs, and releases an adaptable testing framework to guide future design. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13440
Venue
VLDB
Year
2024
Pagerank
4.471975e-05
Overall Rank
8,666 | 39.72%
DOI
10.14778/3659437.3659459

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Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,392 Shapley Value Estimation Based on Differential Matrix 2025 SIGMOD 4.1945683e-05
10,686 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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