activity
20192022
most citedFederated Deep Reinforcement Learning

83 citations · 122 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022★ 6 cited

An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application

Youlong Ding, Xueyang Wu, Zhitao Li +5

Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network…

cs.LG2022★ 2 cited

WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning

Xueyang Wu, Shengqi Tan, Qian Xu +1

Federated learning, as a privacy-preserving collaborative machine learning paradigm, has been gaining more and more attention in the industry. With the huge rise in demand, there h…

cs.LG2020★ 23 cited

Graph Random Neural Network for Semi-Supervised Learning on Graphs

Wenzheng Feng, Jie Zhang, Yuxiao Dong +6

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer fro…

cs.CL2019★ 8 cited

L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition

Yuanfeng Song, Di Jiang, Xuefang Zhao +4

Modern Automatic Speech Recognition (ASR) systems primarily rely on scores from an Acoustic Model (AM) and a Language Model (LM) to rescore the N-best lists. With the abundance of…

cs.LG2019★ 83 cited

Federated Deep Reinforcement Learning

Hankz Hankui Zhuo, Wenfeng Feng, Yufeng Lin +2

In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited. Despite the success of…