Federated learning (FL)isapopular distributedcomputational setting where training isperformed locally or privately [30, 36] and where hyperparameter tuning has been identified as a critical problem[18].

However, these researches are mostly confined to pre-training at the instance-level or single-video tracklet-level. They ignore the identity-invariance in images of the same person across different videos, which is a key focus in person re-identification. To address this issue, we propose a Cross-video Identity-cOrrelating pre-traiNing (CION) framework.

Figure 1: Fourteen queries used in the experiments. They do not contain personally identifiable information or offensive content. All the models are implemented in Pytorch [5] and based on the official implementation of BETAE [6]2 for a fair comparison. Forall the modules using multi-layer perceptron (MLP), we use a three-layer MLP with 1600 hidden neurons and ReLU activation. We apply dropout to the min function inCardMin and search the dropout rate in{0.05,0.10,0.15,0.20}.

Query embedding (QE)--which aims to embed entities and first-order logical (FOL) queries inlow-dimensional spaces--has showngreatpowerinmulti-hop reasoningoverknowledgegraphs.