25 citations · 31 across the 4 of their papers we have counts for
4 papers
Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
Zhufeng Lu, Chentao Jia, Ming Hu +2
As a promising privacy-aware collaborative model training paradigm, Federated Learning (FL) is becoming popular in the design of distributed recommender systems. However, Federated…
FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios
Zekai Chen, Chentao Jia, Ming Hu +3
Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable…
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
Chentao Jia, Ming Hu, Zekai Chen +4
Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classifica…
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Yanhong Fei, Xian Wei, Yingjie Liu +2
Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e.g., feature redundancy, and vanishing…