3 citations · 9 across the 4 of their papers we have counts for
6 papers
Asynchronous Federated Learning for Sensor Data with Concept Drift
Yujing Chen, Zheng Chai, Yue Cheng +1
Federated learning (FL) involves multiple distributed devices jointly training a shared model without any of the participants having to reveal their local data to a centralized ser…
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
Zheng Chai, Yujing Chen, Ali Anwar +3
Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new trade…
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating minimization methods have recently been proposed as alternatives to the gradient descent for deep neural network optimization. Alternating minimization methods can typi…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…
Federated Multi-task Hierarchical Attention Model for Sensor Analytics
Yujing Chen, Yue Ning, Zheng Chai +1
Sensors are an integral part of modern Internet of Things (IoT) applications. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from th…
Characterizing Co-located Datacenter Workloads: An Alibaba Case Study
Yue Cheng, Zheng Chai, Ali Anwar
Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization. To better understand the challenges…