Jellyfish enables zero-shot federated unlearning through synthetic proxy data generation, channel-restricted knowledge disentanglement, and a composite loss with repair to forget target data while retaining model utility.
Proceedings of the IEEE86(11), 2278–2324 (1998)
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Machine unlearning methods adapted to hybrid quantum models achieve effective forgetting that varies with circuit depth and entanglement, establishing initial empirical baselines for quantum-aware unlearning.
A CNN-attention model achieves 99.2% accuracy on seen MRI sites and 75.5% on unseen heterogeneous sites for motion artifact quality assessment.
citing papers explorer
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Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
Jellyfish enables zero-shot federated unlearning through synthetic proxy data generation, channel-restricted knowledge disentanglement, and a composite loss with repair to forget target data while retaining model utility.
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Machine Unlearning in the Era of Quantum Machine Learning: An Empirical Study
Machine unlearning methods adapted to hybrid quantum models achieve effective forgetting that varies with circuit depth and entanglement, establishing initial empirical baselines for quantum-aware unlearning.
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Attention-Gated Convolutional Networks for Scanner-Agnostic Quality Assessment
A CNN-attention model achieves 99.2% accuracy on seen MRI sites and 75.5% on unseen heterogeneous sites for motion artifact quality assessment.