21 citations · 37 across the 11 of their papers we have counts for
4 papers · 1 filter
FedH2L: Federated Learning with Model and Statistical Heterogeneity
Yiying Li, Wei Zhou, Huaimin Wang +2
Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. Mainstream FL approaches req…
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Wei Zhou, Yiying Li, Yongxin Yang +2
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic's action-value function is updated using temporal-di…
Learning data augmentation policies using augmented random search
Mingyang Geng, Kele Xu, Bo Ding +2
Previous attempts for data augmentation are designed manually, and the augmentation policies are dataset-specific. Recently, an automatic data augmentation approach, named AutoAugm…
Collaborative Deep Learning Across Multiple Data Centers
Kele Xu, Haibo Mi, Dawei Feng +4
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter da…