Research map / Federated learning and privacy / Privacy attacks and secure learning
Fast Distributed Submodular Cover: Public-Private Data Summarization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase number of machines m (distributed FastCover) | FastCover · Theory / Friendster | number of MapReduce rounds | Helped |
| Decrease epsilon (finer threshold steps) in FastCover | FastCover · Personalized location recommendation | solution set size | Helped |
| Replace per-user separate summaries with a single joint FastCover summary | FastCover · Movie recommendation (1K movies, 1K users) | solution set size | Helped |
| Use FastCover instead of DisCover (avoid doubling steps) | Distributed submodular cover · Personalized location recommendation | solution set size and number of rounds | Helped |
| Use FastCover instead of DisCover on Friendster dominating set | Distributed submodular cover · Friendster dominating set (50%) | solution set size and rounds | Helped |
Closest papers by meaning
- Fast Constrained Submodular Maximization: Personalized Data Summarization (ICML 2016 · similarity 0.90)
- Differentially Private Submodular Maximization: Data Summarization in Disguise (ICML 2017 · similarity 0.89)
- Differentially Private Distributed Data Summarization under Covariate Shift (NeurIPS 2019 · similarity 0.86)
- Private and Personalized Frequency Estimation in a Federated Setting (NeurIPS 2024 · similarity 0.84)
- Scalable Private Partition Selection via Adaptive Weighting (ICML 2025 · similarity 0.83)
- Efficiently Computing Similarities to Private Datasets (ICLR 2024 · similarity 0.82)
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