103 citations · 365 across the 26 of their papers we have counts for
6 papers · 1 filter
Toward Understanding the Influence of Individual Clients in Federated Learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng +4
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guaran…
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
Liyi Guo, Rui Lu, Haoqi Zhang +9
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and sho…
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
Yuben Qu, Chao Dong, Jianchao Zheng +4
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the…
UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks
Chao Dong, Yun Shen, Yuben Qu +3
The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to i…
Distributed Optimization over Block-Cyclic Data
Yucheng Ding, Chaoyue Niu, Yikai Yan +5
We consider practical data characteristics underlying federated learning, where unbalanced and non-i.i.d. data from clients have a block-cyclic structure: each cycle contains sever…
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan, Chaoyue Niu, Yucheng Ding +5
Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work…