Nguyen H. Tran
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Nguyen H. Tran

Nguyen H. Tran 

Nguyen Hoang Tran (Trần Hoàng Nguyên in Vietnamese)
Associate Professor
School of Computer Science
The University of Sydney

Contact

Rm 428, J12 Building, The University of Sydney,
1 Cleveland Street, Darlington, NSW 2006, Australia,
E-mail: nguyen[dot]tran[at]sydney[dot]edu[dot]au
Ext: 77221

News

  • Hot! I am promoted to Associate Professor (01/23).

  • Hot! “Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks” accepted by IEEE INFOCOM 2023 (12/22).

  • Hot! I am invited as a TPC member of ACM SIGMETRICS’23 (11/22).

  • Hot! I am invited as an Editor of the newly founded journal IEEE Transactions on Machine Learning in Communications Networking (08/22).

  • I receive the prestigious Sydney Research Accelerator (SOAR) Prize 2022-2023 (12/21).

  • I'm invited for an expert interview on the feature topic of ‘‘Federated Learning over Wireless Networks’’ by TCCN Newsletter (11/21).

  • I'm invited as a Keynote Speaker at EAI WiCON 2021 (02/21).

  • ‘‘FEDL’’ is accepted by IEEE/ACM Transactions on Networking (10/20).

  • ‘‘Federated Learning over Wireless Networks: Optimization Model Design and Analysis" is top listed ‘‘popular’’ by IEEE INFOCOM. (09/20)

  • ‘‘Personalized Federated Learning with Moreau Envelopes" accepted by NeurIPS 2020 (09/20).

  • I'm invited as Associate Editor of IEEE JSAC series on Machine Learning for Communications and Networks, in the area of Distributed/Federated Learning and Communications (07/20).

  • Our ARC Discovery Project 2020 got funded. (12/19).

Selected Publications (Full Publications)

  • Federated/Distributed Machine Learning

  1. Tung-Anh Nguyen, Jiayu He, Long Tan Le, Wei Bao, Nguyen H. Tran, ‘‘Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks," [pdf] [code], IEEE INFOCOM, 2023.

  2. Tuan Dung Nguyen, Amir Balef, Canh T. Dinh, Nguyen H. Tran, Duy T. Ngo, Tuan Anh Le, Phuong L. Vo,‘‘Accelerating Federated Edge Learning," IEEE Communication Letters, Aug. 2021

  3. Canh T. Dinh, Tung T. Vu, Nguyen H. Tran, Minh N. Dao, Hongyu Zhang, ‘‘FedU: A Unified Framework for Federated Multi-Task Learning with Laplacian Regularization," [pdf] [code], 2021.

  4. Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Amir Rezaei Balef, ‘‘DONE: Distributed Approximate Newton-type Method for Federated Edge Learning," [pdf] [code], 2021.

  5. Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, ‘‘Personalized Federated Learning with Moreau Envelopes," [pdf] [code], NeurIPS, 2020.

  6. Canh T. Dinh, Nguyen H. Tran, et al., ‘‘Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation," IEEE/ACM Transactions on Networking, [pdf] [code].

  7. Minh N. H. Nguyen, Shashi Raj Pandey, Kyi Thar, Eui-Nam Huh, Nguyen H. Tran, Mingzhe Chen, Walid Saad, Choong Seon Hong, ‘‘Distributed and Democratized Learning: Philosophy and Research Challenges," IEEE Computational Intelligence Magazine, 2020 [pdf] [blog].

  8. Minh N. H. Nguyen, Nguyen H. Tran, Yan Tun, Zhu Han, Choong Seon Hong, ‘‘Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks," 2020.

  9. Minh N. H. Nguyen, Shashi Raj Pandey, Tri Nguyen Dang, Eui-Nam Huh, Choong Seon Hong, Nguyen H. Tran, Walid Saad, ‘‘Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems," [pdf].

  10. Tung T. Vu, Duy T. Ngo, Hien Quoc Ngo, Minh N. Dao, Nguyen H. Tran, Richard H. Middleton, ‘‘User Selection Approaches to Mitigate the Straggler Effect for Federated Learning on Cell-Free Massive MIMO Networks" [pdf].

  11. Zhengjie Yang, Wei Bao, Dong Yuan, Nguyen H. Tran, and Albert Y. Zomaya, ‘‘Federated Learning with Nesterov Accelerated Gradient Momentum Method’’ [pdf].

  12. Tra Le, Nguyen H. Tran, et al., ‘‘An Incentive Mechanism for Federated Learning in Wireless Cellular Network: An Auction Approach’’, IEEE Transactions on Wireless Communication, [pdf].

  13. Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Albert Zomaya, Bing Zhou, ‘‘Federated Learning with Proximal Stochastic Variance Reduced Gradient Algorithms’’, ICPP 2020, Edmonton, Canada, Aug. 2020, [pdf] [video].

  14. Tung T. Vu, Duy T. Ngo, Nguyen H. Tran, Hien Quoc Ngo, Minh N. Dao, Richard H. Middleton, ‘‘Cell-Free Massive MIMO for Wireless Federated Learning’’, IEEE Transactions on Wireless Communication, [pdf].

  15. Shashi Raj Pandey, Nguyen H. Tran, Mehdi Bennis, Yan Kyaw Tun, Aunas Manzoor, Choong Seon Hong ‘‘A Crowdsourcing Framework for On-Device Federated Learning’’, IEEE Transactions on Wireless Communication, 2020, [pdf].

  16. Latif, Nguyen H. Tran, et al., ‘‘Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism’’, IEEE Communication Magazine, 2020, [pdf].

  17. Nguyen H. Tran, Wei Bao, Albert Zomaya, Minh N.H. Nguyen, Choong Seon Hong, ‘‘Federated Learning over Wireless Networks: Optimization Model Design and Analysis", IEEE INFOCOM 2019, Paris, France, April 2019, [pdf] [code].

About me

Nguyen H. Tran (S’10-M’11-SM’18) received BS and PhD degrees (with best PhD thesis award in 2011), from HCMC University of Technology and Kyung Hee University, in electrical and computer engineering, in 2005 and 2011, respectively. Dr Tran is an Associate Professor at the School of Computer Science, The University of Sydney. He was an Assistant Professor with Department of Computer Science and Engineering, Kyung Hee University, from 2012 to 2017. His research group has special interests in Distributed compUting, optimizAtion, and machine Learning (DUAL group). He received several best paper awards, including IEEE ICC 2016 and ACM MSWiM 2019. He receives the Korea NRF Funding for Basic Science and Research 2016-2023, ARC Discovery Project 2020-2023, and SOAR award 2022-2023. He serves as an Editor for several journals such as IEEE Transactions on Green Communications and Networking (2016-2020), IEEE Journal of Selected Areas in Communications 2020 in the area of distributed machine learning/Federated Learning, and IEEE Transactions on Machine Learning in Communications Networking (2022-).