@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
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
@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
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
2022
Blockchain-empowered secure federated learning system: Architecture and applications
Feng Yu
,
Hui Lin,
Xiaoding Wang,
Abdussalam Yassine,
and M. Shamim Hossain
@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}}