3 papers
cs.LG2026
Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs
Yuxuan Liu, Wenchao Xu, Haozhao Wang +5
Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods a…
cs.DC2026
Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
Chongyang Xu, Christoph Siebenbrunner, Laurent Bindschaedler
Cross-partition edges dominate the cost of distributed GNN training: fetching remote features and activations per iteration overwhelms the network as graphs deepen and partition co…
cs.RO2025
RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies
Adina Yakefu, Bin Xie, Chongyang Xu +34
Testing on real machines is indispensable for robotic control algorithms. In the context of learning-based algorithms, especially VLA models, demand for large-scale evaluation, i.e…