Previous works have implicitly studied that FL suffers from the "client-drift" problem, which is caused by the inconsistent optimum across local clients.

We first define the restricted Cheeger constant in the link prediction task. Then, according to Proposition 2.1, we have: Then, we can draw the same conclusion with Eq.12, and the Thus, Eq.16 can be simplified to: "sites" Based on the Eq.15 and Eq.17, we can rewrite L The inequality holds due to the assumption. Knowledge discovery: In the 5 random experiments, we add 500 pseudo links in each iteration. The metadata information of the nodes are all strongly relevant to "Linux" Both papers focus on the "malware"/"phishing" under the topic "Computer security". The detailed result of the case study is shown in Table 6.

Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process.

We start from an intuition that many complex visual data, e.g., videos and 3D scenes, are represented

With the rapidly increasing demand for oriented object detection, e.g. in autonomous driving and remote sensing, the recently proposed paradigm involving