In quantum networks with randomly distributed initial entanglement, classical entanglement percolation depends only on the average value while quantum protocols degrade with increasing distribution width.
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This survey introduces a taxonomy for quantization in federated learning organized around client heterogeneity, aggregation consistency, non-IID robustness, privacy integration, and hardware co-optimization, while analyzing interactions with core FL behaviors.
A survey of quantum adversarial machine learning covering attacks, countermeasures, theoretical underpinnings, trends, and challenges.
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Entanglement percolation in random quantum networks
In quantum networks with randomly distributed initial entanglement, classical entanglement percolation depends only on the average value while quantum protocols degrade with increasing distribution width.
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Quantization in Federated Learning: Methods, Challenges and Future Directions
This survey introduces a taxonomy for quantization in federated learning organized around client heterogeneity, aggregation consistency, non-IID robustness, privacy integration, and hardware co-optimization, while analyzing interactions with core FL behaviors.
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Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
A survey of quantum adversarial machine learning covering attacks, countermeasures, theoretical underpinnings, trends, and challenges.