Di Wu

According to our database, Di Wu authored at least 15 papers between 2020 and 2026.

Collaborative distances:

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

2026

DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning.

Yizhou Han, Di Wu, Blesson Varghese

CoRR, March, 2026

Multi-DNN Inference of Sparse Models on Edge SoCs.

Jiawei Luo, Di Wu, Simon Dobson, Blesson Varghese

CoRR, March, 2026

zkVFL: Verifiable Federated Learning for Free-Rider Attacks via Efficient Zero-Knowledge Proofs.

Jiaxi Liu, Lin Sun, Tianyu Kang, Di Wu, Yulun Song, Yunlong Xie, Li Guo

IEEE Internet Things J., 2026

FedFreeze: A dual-phase layer freezing framework for federated learning.

Di Wu, Leon Wong, Blesson Varghese

Future Gener. Comput. Syst., 2026

2025

DeepCon: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation.

Bin Qian, Jiaxu Qian, Zhenyu Wen, Di Wu, Shibo He, Jiming Chen, Rajiv Ranjan

IEEE Trans. Cogn. Commun. Netw., December, 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