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cs.LG2022
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
Ziqing Fan, Yanfeng Wang, Jiangchao Yao +3
The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previou…
cs.LG2022
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Chenxin Xu, Yuxi Wei, Bohan Tang +3
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly c…