Ming Hu

Orcid: 0000-0002-5058-4660

Affiliations:

Nanyang Technological University (NTU), School of Computer Science and Engineering, Singapore
East China Normal University, Shanghai Key Laboratory of Trustworthy Computing, China (PhD 2022)

According to our database¹, Ming Hu authored at least 49 papers between 2019 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2025

Smart Contract Fuzzing Towards Profitable Vulnerabilities.

[BibT_eX]

[DOI]

CoRR, January, 2025

2024

Joint Client-and-Sample Selection for Federated Learning via Bi-Level Optimization.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., December, 2024

CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance.

[BibT_eX]

[DOI]

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

FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios.

[BibT_eX]

[DOI]

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

FOSS: Towards Fine-Grained Unknown Class Detection Against the Open-Set Attack Spectrum With Variable Legitimate Traffic.

[BibT_eX]

[DOI]

IEEE/ACM Trans. Netw., October, 2024

Texture Re-Scalable Universal Adversarial Perturbation.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2024

Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

[BibT_eX]

[DOI]

Proc. ACM Softw. Eng., 2024

NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing.

[BibT_eX]

[DOI]

CoRR, 2024

An Empirical Study of Vulnerability Detection using Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Federated Graph Learning with Adaptive Importance-based Sampling.

[BibT_eX]

[DOI]

CoRR, 2024

KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting.

[BibT_eX]

[DOI]

CoRR, 2024

MIP: CLIP-based Image Reconstruction from PEFT Gradients.

[BibT_eX]

[DOI]

CoRR, 2024

Personalized Federated Instruction Tuning via Neural Architecture Search.

[BibT_eX]

[DOI]

CoRR, 2024

FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Software Engineering and Knowledge Engineering, 2024

SampDetox: Black-box Backdoor Defense via Perturbation-based Sample Detoxification.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

SoK: An Exhaustive Taxonomy of Display Issues for Mobile Applications.

[BibT_eX]

[DOI]

Liming Nie

Kabir Sulaiman Said

Ming Hu

Proceedings of the 29th International Conference on Intelligent User Interfaces, 2024

DeFort: Automatic Detection and Analysis of Price Manipulation Attacks in DeFi Applications.

[BibT_eX]

[DOI]

Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024

FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation.

[BibT_eX]

[DOI]

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Architecture-Agnostic Iterative Black-Box Certified Defense Against Adversarial Patches.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2024

AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems.

[BibT_eX]

[DOI]

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

FedMut: Generalized Federated Learning via Stochastic Mutation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Automated Synthesis of Safe Timing Behaviors for Requirements Models Using CCSL.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., December, 2023

AIoTML: A Unified Modeling Language for AIoT-Based Cyber-Physical Systems.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., November, 2023

Accelerating Reinforcement Learning-Based CCSL Specification Synthesis Using Curiosity-Driven Exploration.

[BibT_eX]

[DOI]

IEEE Trans. Computers, May, 2023

AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems.

[BibT_eX]

[DOI]

CoRR, 2023

Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation.

[BibT_eX]

[DOI]

CoRR, 2023

FedMR: Federated Learning via Model Recombination.

[BibT_eX]

[DOI]

CoRR, 2023

Towards Interpretable Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

GitFL: Uncertainty-Aware Real-Time Asynchronous Federated Learning Using Version Control.

[BibT_eX]

[DOI]

Proceedings of the IEEE Real-Time Systems Symposium, 2023

Model-Contrastive Learning for Backdoor Elimination.

[BibT_eX]

[DOI]

Proceedings of the 31st ACM International Conference on Multimedia, 2023

2022

HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation.

[BibT_eX]

[DOI]

CoRR, 2022

GitFL: Adaptive Asynchronous Federated Learning using Version Control.

[BibT_eX]

[DOI]

CoRR, 2022

FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation.

[BibT_eX]

[DOI]

CoRR, 2022

FedMR: Fedreated Learning via Model Recombination.

[BibT_eX]

[DOI]

CoRR, 2022

FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment.

[BibT_eX]

[DOI]

CoRR, 2022

Model-Contrastive Learning for Backdoor Defense.

[BibT_eX]

[DOI]

CoRR, 2022

FedCAT: Towards Accurate Federated Learning via Device Concatenation.

[BibT_eX]

[DOI]

CoRR, 2022

Orthogonal Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

Accelerated synthesis of neural network-based barrier certificates using collaborative learning.

[BibT_eX]

[DOI]

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

2021

Efficient Federated Learning for Cloud-Based AIoT Applications.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021

An Ensemble Learning-Based Cooperative Defensive Architecture Against Adversarial Attacks.

[BibT_eX]

[DOI]

J. Circuits Syst. Comput., 2021

Enumeration and Deduction Driven Co-Synthesis of CCSL Specifications using Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the 42nd IEEE Real-Time Systems Symposium, 2021

2020

DIAVA: A Traffic-Based Framework for Detection of SQL Injection Attacks and Vulnerability Analysis of Leaked Data.

[BibT_eX]

[DOI]

IEEE Trans. Reliab., 2020

Quantitative Timing Analysis for Cyber-Physical Systems Using Uncertainty-Aware Scenario-Based Specifications.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2020

2019

Sample-Guided Automated Synthesis for CCSL Specifications.

[BibT_eX]

[DOI]

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

Ming Hu

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