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The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming.

Figure 1 (right), is the analog plot of Figure 1 (right), where the "random configuration" shows the statistics

To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution.

Let θ and φ denote the optimal parameters after model training. The detailed statistics of three datasets used in this paper are listed in Table 1. In this paper, when evaluating the performance in the standard experimental scenario and in the label-scarce scenario, we compare with six state-of-the-art baselines used for graph-based semisupervised learning. Three of them are deterministic GNN-based models, which are GCN [1], Graph Attention Networks(GAT)[2]andGraphSAGE[3]respectively.

Hence, extensiveresearch has been proposed to improvethisbound through richer distributions [55,53,36,13].

Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously.