Jiahui Hu

Orcid: 0000-0001-8771-7474

Affiliations:

Nanchang University, School of Mathematics and Computer Science, Nanchang, China
Zhejiang University, School of Cyber Science and Engineering, Hangzhou, China (PhD 2025)
Wuhan University, School of National Cybersecurity, wuhan, China

According to our database¹, Jiahui Hu authored at least 30 papers between 2018 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Bibliography

2025

Cost-Efficient and Secure Federated Learning for Edge Computing.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., December, 2025

An Incentive Framework for Task Offloading in Edge Computing Marketplaces Under Price Competition.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., September, 2025

Federated Large Language Model: Solutions, Challenges and Future Directions.

[BibT_eX]

[DOI]

IEEE Wirel. Commun., August, 2025

Safeguarding LLM Embeddings in End-Cloud Collaboration via Entropy-Driven Perturbation.

[BibT_eX]

[DOI]

CoRR, March, 2025

Poisoning Attacks to Knowledge Distillation-Based Federated Learning Under Robust Aggregation Rules.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2025

PoiSAFL: Scalable Poisoning Attack Framework to Byzantine-resilient Semi-asynchronous Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the 34th USENIX Security Symposium, 2025

SoK: On Gradient Leakage in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the 34th USENIX Security Symposium, 2025

Textual Unlearning Gives a False Sense of Unlearning.

[BibT_eX]

[DOI]

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Does Differential Privacy Really Protect Federated Learning From Gradient Leakage Attacks?

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., December, 2024

When Federated Learning Meets Knowledge Distillation.

[BibT_eX]

[DOI]

IEEE Wirel. Commun., October, 2024

Shield Against Gradient Leakage Attacks: Adaptive Privacy-Preserving Federated Learning.

[BibT_eX]

[DOI]

IEEE/ACM Trans. Netw., April, 2024

Location Privacy-Aware Task Offloading in Mobile Edge Computing.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., March, 2024

Label-Free Poisoning Attack Against Deep Unsupervised Domain Adaptation.

[BibT_eX]

[DOI]

IEEE Trans. Dependable Secur. Comput., 2024

SoK: Gradient Leakage in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2024

FaceObfuscator: Defending Deep Learning-based Privacy Attacks with Gradient Descent-resistant Features in Face Recognition.

[BibT_eX]

[DOI]

Proceedings of the 33rd USENIX Security Symposium, 2024

Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments.

[BibT_eX]

[DOI]

Proceedings of the IEEE INFOCOM 2024, 2024

Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE INFOCOM 2024, 2024

2023

Towards Privacy-Driven Truthful Incentives for Mobile Crowdsensing Under Untrusted Platform.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., 2023

Threats to Training: A Survey of Poisoning Attacks and Defenses on Machine Learning Systems.

[BibT_eX]

[DOI]

ACM Comput. Surv., 2023

Privacy-preserving Adversarial Facial Features.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Towards Efficient Edge Learning for Large Models in Heterogeneous Resource-limited Environments.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Big Data Computing and Communications, 2023

2022

Privacy-preserving task allocation for edge computing-based mobile crowdsensing.

[BibT_eX]

[DOI]

Comput. Electr. Eng., 2022

2021

Towards Personalized Task-Oriented Worker Recruitment in Mobile Crowdsensing.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., 2021

2020

Towards Demand-Driven Dynamic Incentive for Mobile Crowdsensing Systems.

[BibT_eX]

[DOI]

IEEE Trans. Wirel. Commun., 2020

2019

Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., 2019

When Mobile Crowdsensing Meets Privacy.

[BibT_eX]

[DOI]

IEEE Commun. Mag., 2019

Task Bundling Based Incentive for Location-Dependent Mobile Crowdsourcing.

[BibT_eX]

[DOI]

IEEE Commun. Mag., 2019

Towards Privacy-preserving Incentive for Mobile Crowdsensing Under An Untrusted Platform.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE Conference on Computer Communications, 2019

2018

Heterogeneous incentive mechanism for time-sensitive and location-dependent crowdsensing networks with random arrivals.

[BibT_eX]

[DOI]

Comput. Networks, 2018

Pay On-Demand: Dynamic Incentive and Task Selection for Location-Dependent Mobile Crowdsensing Systems.

[BibT_eX]

[DOI]

Proceedings of the 38th IEEE International Conference on Distributed Computing Systems, 2018

Jiahui Hu

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