In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, which we say is a "dual drift" caused by dual

Topic models have been evolving rapidly over the years, from conventional to recent neural models.

Among all the contributing factors, the quality and selection of data is becoming increasingly recognized for its importance in training LLMs effectively.

To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set.

's matching capacity will be reduced

Despite these strides, evaluating these models poses substantial challenges.