Zirui Lian

According to our database, Zirui Lian authored at least 8 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Arch2End: Two-Stage Unified System-Level Modeling for Heterogeneous Intelligent Devices.

Weihong Liu, Zongwei Zhu, Boyu Li, Yi Xiong, Zirui Lian, Jiawei Geng, Xuehai Zhou

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., November, 2024

NebulaFL: Self-Organizing Efficient Multilayer Federated Learning Framework With Adaptive Load Tuning in Heterogeneous Edge Systems.

Zirui Lian, Jing Cao, Qianyue Cao, Weihong Liu, Zongwei Zhu, Xuehai Zhou

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., November, 2024

GOFL: An Accurate and Efficient Federated Learning Framework Based on Gradient Optimization in Heterogeneous IoT Systems.

Zirui Lian, Jing Cao, Zongwei Zhu, Xuehai Zhou, Weihong Liu

IEEE Internet Things J., April, 2024

Ace-Sniper: Cloud-Edge Collaborative Scheduling Framework With DNN Inference Latency Modeling on Heterogeneous Devices.

Weihong Liu, Jiawei Geng, Zongwei Zhu, Yang Zhao, Cheng Ji, Changlong Li, Zirui Lian, Xuehai Zhou

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., February, 2024

2022

FedNorm: An Efficient Federated Learning Framework with Dual Heterogeneity Coexistence on Edge Intelligence Systems.

Zirui Lian, Weihong Liu, Jing Cao, Zongwei Zhu, Xuehai Zhou

Proceedings of the IEEE 40th International Conference on Computer Design, 2022

Sniper: cloud-edge collaborative inference scheduling with neural network similarity modeling.

Weihong Liu, Jiawei Geng, Zongwei Zhu, Jing Cao, Zirui Lian

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

2021

AGQFL: Communication-efficient Federated Learning via Automatic Gradient Quantization in Edge Heterogeneous Systems.

Zirui Lian, Jing Cao, Yanru Zuo, Weihong Liu, Zongwei Zhu

Proceedings of the 39th IEEE International Conference on Computer Design, 2021

HADFL: Heterogeneity-aware Decentralized Federated Learning Framework.

Jing Cao, Zirui Lian, Weihong Liu, Zongwei Zhu, Cheng Ji

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021