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New gradient inversion attacks exploit subspace constraints in federated learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new gradient inversion attacks for federated learning that leverage linear and affine subspace constraints. These methods improve the accuracy of reconstructing sensitive client data from exchanged gradients. The study also found that moderate overestimation of batch size can enhance reconstruction quality, even with unknown true batch sizes, and examined the impact of differential privacy mechanisms on attack effectiveness. AI

IMPACT Enhances understanding of privacy risks in federated learning and potential defenses against data reconstruction.

RANK_REASON Academic paper detailing a new method for gradient inversion attacks in federated learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New gradient inversion attacks exploit subspace constraints in federated learning

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Academic paper detailing a new method for gradient inversion attacks in federated learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Konstantinos Bouros, Athanasios P. Liavas ·

    Gradient Inversion through Linear and Affine Subspace Constraints

    arXiv:2611.13976v1 Announce Type: cross Abstract: Federated learning enables multiple clients to collaboratively train a machine-learning model without directly sharing their local datasets. However, as prior works have shown, the gradients exchanged during training may reveal se…

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