src_fb50be84b835134c75
- Source-provided label
- Not reported
- Evidence class
- mentioned in report
- Confidence
- Not reported
- Observed
- Oct 07, 2026 04:00 UTC
- Retrieved
- Oct 08, 2026 03:43 UTC
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
arXiv:2508.19819v3 Announce Type: replace Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable experimental settings. However, it remains unclear whether such attacks are feasible in modern, performance-optimized systems deployed in practice. In this work, we evalua
Reference details
- Source category
- Research & news
- Report title
Practical Feasibility of Gradient Inversion Attacks in Federated Learning- First reported
- Oct 07, 2026 04:00 UTC
- Last reported
- Oct 07, 2026 04:00 UTC
- Catalog updated
- Oct 08, 2026 03:43 UTC
- Record ID
fc5242cb7b1070218f0925ce5302ba2a5b77e75c61ea6ea2b1fe2699c4de4152
Published indicators
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Source observations
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Feed collection status
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1 sources: 0 stale, 0 needing attention, 0 paused, 1 unverified.
Review source update dates and status
- src_fb50be84b835134c75Collection status unverifiedLast successful batch: Unverified
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