Feng Yu | publications

Preprints

2025

Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation

Feng Yu , Jia Hu, and Geyong Min

arXiv preprint arXiv:2505.12318 2025

Bib HTML
@article{yu2025efficient, title = {Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation}, author = {Yu, Feng and Hu, Jia and Min, Geyong}, journal = {arXiv preprint arXiv:2505.12318}, year = {2025}, bibtex_show = {true}, selected = {true}, html = {https://arxiv.org/abs/2505.12318} }

2024

Federated Continual Learning for Edge-AI: A Comprehensive Survey

Zi Wang, Fei Wu, Feng Yu , Yurui Zhou, Jia Hu, and Geyong Min

arXiv preprint arXiv:2411.13740 2024

Bib HTML
@article{zi2024federated, title = {Federated Continual Learning for Edge-AI: A Comprehensive Survey}, author = {Wang, Zi and Wu, Fei and Yu, Feng and Zhou, Yurui and Hu, Jia and Min, Geyong}, journal = {arXiv preprint arXiv:2411.13740}, year = {2024}, bibtex_show = {true}, selected = {true}, html = {https://arxiv.org/abs/2411.13740} }

Papers

2023

Communication-Efficient Personalized Federated Meta-Learning in Edge Networks

Feng Yu , Hui Lin, Xiaoding Wang, Sahil Garg, Georges Kaddoum, Satinder Singh, and Mohammad Mehedi Hassan

IEEE Transactions on Network and Service Management 2023

Bib HTML
@article{yu_communication-efficient_2023, title = {Communication-Efficient Personalized Federated Meta-Learning in Edge Networks}, copyright = {4.758}, issn = {1932-4537}, doi = {10.1109/TNSM.2023.3263831}, journal = {IEEE Transactions on Network and Service Management}, author = {Yu, Feng and Lin, Hui and Wang, Xiaoding and Garg, Sahil and Kaddoum, Georges and Singh, Satinder and Hassan, Mohammad Mehedi}, year = {2023}, pages = {1--1}, html = {https://doi.org/10.1109/tnsm.2023.3263831}, bibtex_show = {true}, abbr = {TNSM}, selected = {true} }
An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems

Ankita. Sharma, Shalli Rani, Syed Hassan Shah, Rohit Sharma, Feng Yu , and Mohammad Mehedi Hassan

IEEE Transactions on Network Science and Engineering 2023

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Security is critical in the Cyber-Physical Systems (CPS) model for smart healthcare networks, and it will likely have a significant impact on the industry, medical and healthcare; and farming-related substructures shortly. Due to an increase in the frequency of security and privacy attacks in present times in healthcare networks, this article addressed a fundamental component of intrusion detection systems (IDS) based on the important parameter security. The limitations of IDS in reacting to cyberattacks as well as in establishing private controls in the field of smart healthcare have motivated this research. An efficient and lightweight deep learning-based CNN-Bidirectional LSTM is proposed for the DDoS detection that uses the features of Convolutional Neural Networks (CNNs) to classify traffic flows as benign and malicious in this study. The results are achieved using Python where four convolutional layers, Maximum Pooling, that ends with the Dense Layer. The hyperparameters used are batch size of 500, epochs 20, number of classes 25, and Relu and softmax pooling activation function along with the softmax
@article{sharma_efficient_2023, title = {An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems}, copyright = {5.033}, issn = {2327-4697}, doi = {10.1109/TNSE.2023.3273301}, journal = {IEEE Transactions on Network Science and Engineering}, author = {Sharma, Ankita. and Rani, Shalli and Shah, Syed Hassan and Sharma, Rohit and Yu, Feng and Hassan, Mohammad Mehedi}, year = {2023}, note = {Conference Name: IEEE Transactions on Network Science and Engineering}, keywords = {Security, Internet of Things, Deep learning, Feature extraction, Deep Learning, Intrusion detection, Healthcare, Medical services, To-Read, /unread, CNN-LSTM, Cyber-Physical System, Cyber-physical systems, DDoS Attack}, pages = {1--10}, html = {https://doi.org/10.1109/TNSE.2023.3273301}, bibtex_show = {true}, abbr = {TNSE}, selected = {false} }

2022

Blockchain-empowered secure federated learning system: Architecture and applications

Feng Yu , Hui Lin, Xiaoding Wang, Abdussalam Yassine, and M. Shamim Hossain

Computer Communications 2022

Bib HTML PDF
@article{YU202255, title = {Blockchain-empowered secure federated learning system: Architecture and applications}, journal = {Computer Communications}, volume = {196}, pages = {55-65}, year = {2022}, issn = {0140-3664}, doi = {https://doi.org/10.1016/j.comcom.2022.09.008}, url = {https://www.sciencedirect.com/science/article/pii/S0140366422003474}, author = {Yu, Feng and Lin, Hui and Wang, Xiaoding and Yassine, Abdussalam and Hossain, M. Shamim}, html = {https://doi.org/10.1016/j.comcom.2022.09.008}, pdf = {bfl_comcom2022.pdf}, bibtex_show = {true}, abbr = {COMCOM}, selected = {true} }