Publications

Published Book:

[B1] Ahmed Imteaj, M. Hadi Amini, and Panos M. Pardalos, "Foundations of Blockchain: Theory and Applications", Springer, 2021. (Invited and recognized at FIU Book Authors Recognition Ceremony) 

Recent Peer-Reviewed Journal Publications

• Moore, Ervin, Ahmed Imteaj, Shabnam Rezapour, and M. Hadi Amini. ``A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing." Accepted in IEEE Internet of Things Journal, (2023).  [Impact factor: 10.238]

• Ahmed Imteaj and M. Hadi Amini. “FedMDP: A Federated Learning Framework to Handle Model and System Heterogeneity via Knowledge Distillation and Dynamic Local Task Allocation.” under review in IEEE Transactions on Artificial Intelligence.

• Ahmed Imteaj, A. R. Shahid and S. Zaman, ``Leveraging Blockchain Interoperability for Interdependent Networks," in IEEE Consumer Electronics Magazine, doi: 10.1109/MCE.2023.3245283. [Impact Factor: 4.135]  

• Ahmed Imteaj,Vahid Akbari, and Mohammad Hadi Amini. 2023. "A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems" Sensors 23, no. 3: 1200. https://doi.org/10.3390/s23031200 (Impact factor: 3.847) [Link]

• Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li and M. Hadi Amini, “A survey on Federated Learning for Resource-Constrained IoT Devices”, in IEEE Internet of Things Journal, 2021. (Impact factor: 10.238)

• Ahmed Imteaj and M. Hadi Amini, “FedPARL: Client Activity and Resource-Oriented Lightweight Federated Learning Model for Resource-Constrained Heterogeneous IoT Environment,” Frontiers in Communications and Networks 2 (2021): 10. (One of the Two Winners at FIU GSAW Scholarly Forum 2021)

• Ahmed Imteaj, Irfan Khan, Javad Khazaei, M. Hadi Amini, “FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures,” Electronics 2021, 10(16):1917, 2021. (Impact factor: 2.69) (Collaborative research work with Texas A&M University and Lehigh University)

• *M. Hyman, *C. Mark, *Ahmed Imteaj, Hamed Ghiaie, Shabnam Rezapour, Arif M. Sadri, M. Hadi Amini, “Data Analytics to Evaluate the Impact of Infectious Disease on Economy: Case Study of COVID-19 Pandemic,” Patterns Journal (2021). [*Authors contributed equally] [Featured by FIU NEWS, FIU KFSCIS]

• Ahmed Imteaj and M. Hadi Amini. "Leveraging asynchronous federated learning to predict customers’ financial distress." Intelligent Systems with Applications 14 (2022): 200064 (Elsevier).

• M. Hadi Amini, Ahmed Imteaj, and Panos Pardalos, “Interdependent Networks: A Data Science Perspective”, Patterns Journal (2020). (Collaborative research work with University of Florida)

• MSMH Najat, Ahmed Imteaj, and Md Hossain. "A Novel Block Cipher Based on Randomly Shuffled Key Strings." Arabian Journal for Science and Engineering 45, no. 12 (2020): 10975-10987. (Impact factor: 2.807)

Recent Conference Publications

• Shahid, Abdur R., Ahmed Imteaj, Shahriar Badsha, and Md Zarif Hossain. "Assessing Wearable Human Activity Recognition Systems Against Data Poisoning Attacks in Differentially-Private Federated Learning." In 2023 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 355-360. IEEE, 2023.

• Ahmed Imteaj, and M. Hadi Amini. ``FedMDP: A Federated Learning Framework to Handle System and Model Heterogeneity in Resource-Constrained Environments," AI2SE, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23) (2023).

• Md Zarif Hossain, Ahmed Imteaj, Saika Zaman, Abdur R. Shahid, Sajedul Talukder, M. Hadi Amini,``FLID: Intrusion Attack and Defense Mechanism for Federated Learning Empowered Connected Autonomous Vehicles (CAVs) Application," Accepted in 2023 IEEE Conference on Dependable and Secure Computing (IEEE DSC'23).

• Shahid, Abdur R., Ahmed Imteaj, Peter Y. Wu, Diane A. Igoche, and Tauhidul Alam. ``Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System." In 2022 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 908-914. IEEE, 2022.

• Ahmed Imteaj, Raghad Alabagi, and M. Hadi Amini. "Exploiting Federated Learning Technique to Recognize Human Activities in Resource-Constrained Environment." In Intelligent Human Computer Interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20–22, 2021, Revised Selected Paper, pp. 659-672, Springer, Cham, 2021. (Winner of IHCI’21 Student Travel Scholarship Award and FIU GPSC Travel Grant)

• Ahmed Imteaj, and M. Hadi Amini. "FedAR: Activity and resource-aware federated learning model for distributed mobile robots." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1153-1160. IEEE, 2020. (Basis of a recently Awarded $300,000 Research Grant from the US Department of Homeland Security)

• Ahmed Imteaj, M. Hadi Amini, Distributed Sensing Using Smart End-user Devices: Pathway to Federated Learning for Autonomous IoT, 2019 IEEE Conference on Computational Science & Computational Intelligence, 2019. (Best Paper Award Winner)

• M. Hadi Amini, Laurent L. Njilla, Ahmed Imteaj, and Calvin Mark. "Distributed Network Optimization for Secure Operation of Interdependent Complex Networks." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1777-1782. IEEE, 2021. (Recipient of FIU GPSC Travel Grant)

• Ahmed Imteaj, M. Hadi Amini, and Javad Mohammadi. “Leveraging decentralized artificial intelligence to enhance resilience of energy networks.” In 2020 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5. IEEE, 2020. (Collaborative research work with Carnegie Mellon University)

• Ahmed Imteaj, Distributed machine learning for collaborative mobile robots: PhD forum abstract, in Proceedings of the 18th Conference on Embedded Networked Sensor Systems (SenSys’20), Association for Computing Machinery, New York, NY, USA, 798799, 2020.

• M. Hadi Amini, Laurent L. Njilla, Ahmed Imteaj, and Calvin Mark. "Distributed Network Optimization for Secure Operation of Interdependent Complex Networks." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1777-1782. IEEE, 2021. (Received FIU GPSC Travel Grant, and supported by US Air Force Research Lab)

• Khandaker Mamun Ahmed, Ahmed Imteaj, and M. Hadi Amini. "Federated Deep Learning for Heterogeneous Edge Computing." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1146-1152. IEEE, 2021.

• M. Hadi Amini, Ahmed Imteaj, and Javad Mohammadi. "Distributed machine learning for resilient operation of electric systems." In 2020 International Conference on Smart Energy Systems and Technologies (SEST), pp. 1-6. IEEE, 2020. (Collaborative research work with Carnegie Mellon University)

• Ahmed Imteaj, Khandaker Mamun Ahmed, Urmish Thakker, Shiqiang Wang, Jian Li, and M. Hadi Amini, “Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the art,” in Federated and Transfer Learning (FTL’21).

• Mohammadi, Farid Ghareh, Ahmed Imteaj, M. Hadi Amini, and Hamid R. Arabnia. “Human Motion Recognition Using Zero-Shot Learning.” In Advances in Artificial Intelligence and Applied Cognitive Computing, pp. 171-181. Springer, Cham, 2021. (Collaborative research work with University of Georgia)

• Syed Rahman, Ahmed Imteaj, Irfan Khan, M. Hadi Amini, “Cascaded Solid State Transformer Structure to Power Fast EV Charging Stations from Medium Voltage Transmission Lines”, in Proceedings of the 54th Annual Asilomar Conference on Signals, Systems, and Computers (Asilomar 2020), USA. (Collaborative research work with Texas A&M University)

• Abdur R. Shahid, Niki Pissinou, Laurent Njilla, Sheila Alemany, Ahmed Imteaj, Kia Makki, and Edwin Aguilar. 2019. Quantifying location privacy in permissioned blockchain-based internet of things (IoT). In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '19). Association for Computing Machinery, New York, NY, USA, 116–125, 2019. (Supported by US Air Force Research Lab)