Weiqi Wang

Orcid: 0000-0002-7905-3126

Affiliations:
  • University of Technology Sydney, Sydney, NSW, Australia


According to our database1, Weiqi Wang authored at least 14 papers between 2023 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
ROSE: A Receiver-Oriented Semantic Communication Framework.
IEEE Netw., March, 2025

Evaluation of Machine Unlearning Through Model Difference.
IEEE Trans. Inf. Forensics Secur., 2025

SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications.
IEEE Trans. Inf. Forensics Secur., 2025

FedU: Federated Unlearning via User-Side Influence Approximation Forgetting.
IEEE Trans. Dependable Secur. Comput., 2025

CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning.
IEEE Trans. Dependable Secur. Comput., 2025

Backdoored Sample Cleansing for Unlabeled Datasets via Bootstrapped Dual Set Purification.
IEEE Trans. Dependable Secur. Comput., 2025

TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning.
Proceedings of the ACM on Web Conference 2025, 2025

Can Self Supervision Rejuvenate Similarity-Based Link Prediction?
Proceedings of the Data Science: Foundations and Applications, 2025

2024
Forgetting and Remembering Are Both You Need: Balanced Graph Structure Unlearning.
IEEE Trans. Inf. Forensics Secur., 2024

OPMUS: A Win-Win Pricing Strategy for Machine Unlearning Service.
Proceedings of the Advanced Data Mining and Applications - 20th International Conference, 2024

UFL: Unlinkable Federated Learning Through Shuffle and Shamir's Secret Sharing.
Proceedings of the Advanced Data Mining and Applications - 20th International Conference, 2024

2023
RUE: Realising Unlearning from the Perspective of Economics.
Proceedings of the 22nd IEEE International Conference on Trust, 2023

FedMC: Federated Learning with Mode Connectivity Against Distributed Backdoor Attacks.
Proceedings of the IEEE International Conference on Communications, 2023

CP-FL: Practical Gradient Leakage Defense in Federated Learning with Compressive Privacy.
Proceedings of the IEEE Global Communications Conference, 2023


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