6 accepted papers
Node-level federated graph clustering allows multiple unlabeled subgraph holders to collaboratively train on node-level tasks without sharing private information. Existing methods usually assume that the node attributes are complete and have achieved promising progress. However, in the Federated Gra
With the rise of vertical segmentation in real-world data, federated graph-level clustering has gained significant attention in recent years. However, the inherent missing attributes in graph datasets held by certain clients lead to suboptimal local parameter updates and misaligned global parameter
In the federated clustering task, structural heterogeneity across clients inevitably impedes effective multi-source information sharing. To solve this issue, Personalized Federated Learning (PFL) has emerged as a potentially effective solution for image and text clustering. Unlike Euclidean data, gr
Locomotion in robots remains an unsolved challenge, particularly for those with complex structures and dynamic environments. Consequently, the control systems for such robots must place greater emphasis on risk mitigation and safety considerations to ensure reliable and stable operation. Existing st
Subgraphs of a complete graph are usually distributed across multiple devices and can only be accessed locally because the raw data cannot be directly shared. However, existing node-level federated graph learning suffers from at least one of the following issues: 1) heavily relying on labeled graph…
Guiding vector fields (GVFs) have been widely applied in robotic path-following control. However, most, if not all, of the existing studies derive control algorithms that only render the path-following error asymptotically converging to zero, while more stringent time constraints on the path-followi