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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
*About Me*
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 1
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portfolio
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publications
Published in conferences and journals, 2018
- Lei, K., Huang, J.1, et al. Semantic Similarity Measures to Disambiguate Terms in Medical Text. In the 25th International Conference on Neural Information Processing (ICONIP 2018, CORE: B).
Published in conferences and journals, 2019
- Huang, J., Du, M., et al. Attention based Updates Aggregation in Federated Learning. In the 28th International Joint Conference on Artificial Intelligence (CORE: A*) workshop on Federated Machine Learning (IJCAI’FL 19).
- Huang, J., Lei, K., et al. Survey on Blockchain Incentive Mechanism. In the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE 2019).
- Shen, Y. Huang J.1, et al. Discovering Medical Entity Relations from Texts using Dependency Information. In the 28th International Joint Conference on Artificial Intelligence (CORE: A*) workshop on Agent-based Complex Automated Negotiations (IJCAI’ACAN 19)
Published in conferences and journals, 2020
- Huang, J., Talbi, R., et al. An Exploratory Analysis on Users’ Contributions in Federated Learning. In IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS 2020).
- Lei, K., Du, M., Huang, J., & Jin, T. Groupchain: Towards a scalable public blockchain in fog computing of IoT services computing. in IEEE Transactions on Services Computing (TSC, CORE: A*).
Published in conferences and journals, 2022
- Huang, J., Hong, C., et al. Tackling mavericks in federated learning via adaptive client selection strategy. In the Association for the Advancement of Artificial Intelligence (AAAI 2022, CORE: A*) International Workshop on Trustable, Verifiable and Auditable Federated Learning.
- Xu, J., Hong, C., Huang, J., et al. Agic: Approximate gradient inversion attack on federated learning. In the 41st International Symposium on Reliable Distributed Systems (SRDS 2022, CORE: B).
Published in conferences and journals, 2023
- Huang, J., Zhao, Z., et al. Fabricated Flips: Poisoning Federated Learning without Data. In the 53th International Conference on Dependable Systems and Networks (DSN 2023, CORE: A).
- Huang, J., Hong, C., et al. Maverick Matters: Client Contribution and Selection in Federated Learning. In the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023, CORE: A).
- Zhao, Z.eq, Huang, J.eq, et al. Defending Against Free-Riders Attacks in Distributed Generative Adversarial Networks. In the 27th International Conference on Financial Cryptography and Data Security (FC 2023, CORE: A).
- Hong, C., Huang, J., et al. Exploring and Exploiting Data-Free Model Stealing. In the 18th the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023, CORE: A)
Published in conferences and journals, 2024
- Huang, J., Roos, S., et al. On Quantifying the Gradient Inversion Risk of Data Reuse in Federated Learning Systems. In the 43rd International Symposium on Reliable Distributed Systems (SRDS 2024, CORE: B).
talks
Talk on Incentives and Attacks in Federated Learning
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Vision track on summary of incentives and attacks in Federated Learning. Here we explore how to evaluate the contribution of a client with the presence of both honest and malicious clients.
Talk on Mavericks in Federated Learning
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This talk discusses one important but overlooked type of heterogeneous client, Maverick, which exclusively owns specific data and we see its broad applications such as rare disease databases. And its contribution is also underestimated by existing measurements.
Talk on Client Selection with Mavericks in Federated Learning
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Followed by the research of the specific heterogeneous client, Maverick, this talk analyzes its contribution and propose a distance-based client selection method with convergence guarantee.
Talk on Data-free Untargeted Attack in Federated Learning
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Existing untargeted attacks in FL require the adversary owns either a large amount of data or eavesdropping all benign updates. This talk introduces our data-free approach to launch attack with two variants, benefiting from effectiveness and stealthiness.
Talk on data-free attacks and defenses
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Talks on how to design effective data-free attacks and corresponding defenses based on synthetic data, in terms of goals, methods, and possible flexible parts for specifc scenarios.
Talk on Training and Inference Time Attack and Defense
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Adversarial behaviors can be launched during different phrases of distributed learning, e.g., Federated Learning, Multi-discriminator GAN. This talk gives a summary on the types of attacks during training and inference phrase, and it introduced four works from us related to it.
Talk on My research road map
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F+cube encourages and helps female researchers in the STEM area for academic careers. In this talk, I provide my research road map and discuss how female researchers takle with difficulties related to gender.
Talk on Hindrance of Trustworthy Distributed Machine Learning
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Radboud Digital Security group Lunch Talk provide a forum for sharing research results. Thanks for the invitation of Prof. Dr. H.K. Schraffenberger (Hanna) and Prof. Dr. S. Picek (Stjepan), I shared my work on hindrance of trustworthy distributed machine learning.
Poster on data reconstruction attack of multi-server FL systems
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Poster titled “On Quantifying Data Reconstruction Risk for Multi-server Federated Learning Systems” in track CompSys Research for a Responsibly Digitalised Society.
teaching
Supervisor for 4-year bachelor project
Bachelor project, Department of EE, Tianjin University, 2017
Solely supervising 30 bachelor students on orientation and answering lessons’ questions
TA for 04711990‑Internet Fintech
Seminar, Department of ECE, Peking University, 2018
Preparing course materials and design assignments for 2 consecutive years.
Co-supervisor for master thesis
Master thesis, Department of EEMCS, TU Delft, 2021
Co-supervising master student Jin Xu for thesis for 6 months, including topic choosing, research question formalization, possible solution and paper writing. This gradient inversion attack of Federated Learning is published on SRDS 2022.
Co-supervisor for bachelor thesis
Bachelor thesis, Department of EEMCS, TU Delft, 2022
Co-supervising 5 bachelor students (Kanish Dwivedi, Joost Jansen, Pietro Vigilanza Lorenzo, Steffano Psathas and Floris van Veen) for thesis for 3 months, including task explanation, research question formalization, QA during solutions, thesis writing and defence preparation.
Guest Lecturer and TA for CS4290
Master course, Department of EEMCS, TU Delft, 2022
Guest Lecturer for CS4290 Seminar on Distributed Machine Learning on the topic of malicious behaviors in Federated Learning. Teaching Assistant for CS4290 Seminar for two years, including choosing research paper for students to review, and grading accordingly.
Supervisor of CSE3000
Bachelor course, Department of EEMCS, TU Delft, 2023
Supervised 4 bachelor students (Todor Mladenović, Quinten Van Opstal, Jan van der Meulen, and Lazar Nenovski) for 3 months, on the course of Malicious Parties and Defenses in Multi-Server Federated Learning with four good thesis projects.
Supervisor for master thesis
Master thesis, Department of EEMCS, TU Delft, 2024
Ongoing supervising master student Caspar for thesis for 6 months, collaborated with TNO, on the topic of gradient inversion attacks on Time-series energy data.
Guest lecturer of 62122 distributed deep learning systems
Master course, Univerisity of Neuchatel, 2024
Guest lecturer for FS2024: 62122 Distributed Deep Learning Systems at the University of Neuchatel on the topic of advanced attacks in distributed learning systems. The lecture includes concepts and preliminaries in attacks and defenses of distributed learning systems. Examples of advanced privacy leakage as well as security risks are discussed. A research-oriented group task of improving gradient inversion attacks is designed.
Guest lecturer of MSc course CS4035 Cyber Data Analytics
Master course, Delft University of Technology, 2024
Guest lecture related to privacy-preserving or secure AI: attacks during both training and inference time.