Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources.

Despite these advancements, QAOA's practical efficacy is challenged by the quantum coherence limits of modern quantum devices, as there is a ceiling on the allowable maximum circuit depth

Adversarial training is an effective methodology to train deep neural networks which arerobustagainstadversarial, norm-bounded perturbations. However,the computational cost of adversarial training grows prohibitively as the size of the model and number of input dimensions increase.

"label shift" assumption, where the labels now also include the nuisance factors

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There is a rapidly growing interest in controlling consistency across multiple generated images using diffusion models.