Federated learning improves segmentation accuracy for hardware reverse engineering but remains vulnerable to recovering proprietary SEM images via gradient inversion attacks.
Sok: On gradient leakage in federated learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
FedHD is a federated learning framework for whole slide images that distills one-to-one synthetic features aligned via Gaussian mixtures and progressively integrates cross-site features through curriculum learning to handle institutional heterogeneity.
citing papers explorer
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Potentials and Pitfalls of Applying Federated Learning in Hardware Assurance
Federated learning improves segmentation accuracy for hardware reverse engineering but remains vulnerable to recovering proprietary SEM images via gradient inversion attacks.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
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Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
FedHD is a federated learning framework for whole slide images that distills one-to-one synthetic features aligned via Gaussian mixtures and progressively integrates cross-site features through curriculum learning to handle institutional heterogeneity.