Di Wu

According to our database, Di Wu authored at least 10 papers between 2020 and 2025.

Collaborative distances:

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

2025

EMO: Edge Model Overlays to Scale Model Size in Federated Learning.

Di Wu, Weibo He, Wanglei Feng, Zhenyu Wen, Bin Qian, Blesson Varghese

CoRR, April, 2025

Edge-Cloud Collaborative Streaming Video Analytics With Multi-Agent Deep Reinforcement Learning.

Bin Qian, Yubo Xuan, Di Wu, Zhenyu Wen, Renyu Yang, Shibo He, Jiming Chen, Rajiv Ranjan

IEEE Netw., January, 2025

FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning.

Zhenyu Wen, Wanglei Feng, Di Wu, Haozhen Hu, Chang Xu, Bin Qian, Zhen Hong, Cong Wang, Shouling Ji

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

2024

EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning.

Di Wu, Rehmat Ullah, Philip Rodgers, Peter Kilpatrick, Ivor T. A. Spence, Blesson Varghese

IEEE Trans. Parallel Distributed Syst., March, 2024

2023

Communication Efficient DNN Partitioning-based Federated Learning.

Di Wu, Rehmat Ullah, Philip Rodgers, Peter Kilpatrick, Ivor T. A. Spence, Blesson Varghese

CoRR, 2023

2022

FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning.

Di Wu, Rehmat Ullah, Paul Harvey, Peter Kilpatrick, Ivor T. A. Spence, Blesson Varghese

IEEE Internet Things J., 2022

FedFly: Toward Migration in Edge-Based Distributed Federated Learning.

Rehmat Ullah, Di Wu, Paul Harvey, Peter Kilpatrick, Ivor T. A. Spence, Blesson Varghese

IEEE Commun. Mag., 2022

FedComm: Understanding Communication Protocols for Edge-based Federated Learning.

Gary Cleland, Di Wu, Rehmat Ullah, Blesson Varghese

Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing, 2022

2021

FedFly: Towards Migration in Edge-based Distributed Federated Learning.

Rehmat Ullah, Di Wu, Paul Harvey, Peter Kilpatrick, Ivor T. A. Spence, Blesson Varghese

CoRR, 2021

2020

EasyQuant: Post-training Quantization via Scale Optimization.

Di Wu, Qi Tang, Yongle Zhao, Ming Zhang, Ying Fu, Debing Zhang

CoRR, 2020