The word embedding space in neural models is skewed, and correcting this can improve task performance.

Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for treating human diseases.

Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SL T datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SL T.

However, a topology with a fast consensus rate, e.g., the exponential graph, generally has a large maximum degree, which incurs significant communication

To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLP A) based on SecAgg protocol.

First, we define distributionally equivalent and Max-Information model extraction attacks, and reduce them into a variational optimisation problem.