Zahidur Talukder

Zahidur Talukder

Ph.D. Candidate in Department of Computer Science at The University of Texas at Arlington

Publications

Enabling Low-Cost Server-Level Power Monitoring in Data Centers Using Conducted EMI

Published in SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems, 2023

Proposed an auto weighted aggregation technique for FL

Recommended citation: Gupta, Pranjol Sen and Talukder, Zahidur and Abir, Tasnim Azad and Nguyen, Phuc and Islam, Mohammad A., "Enabling Low-Cost Server-Level Power Monitoring in Data Centers Using Conducted EMI," Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems, 2023, pp. 237–250, doi: 10.1145/3625687.3625801. https://dl.acm.org/doi/abs/10.1145/3625687.3625801

Computationally Efficient Auto-Weighted Aggregation for Heterogeneous Federated Learning

Published in IEEE International Conference on Edge Computing and Communications (EDGE), 2022

Proposed an auto weighted aggregation techniques for FL

Recommended citation: Z. Talukder and M. A. Islam, "Computationally Efficient Auto-Weighted Aggregation for Heterogeneous Federated Learning," 2022 IEEE International Conference on Edge Computing and Communications (EDGE), 2022, pp. 12-22, doi: 10.1109/EDGE55608.2022.00015. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9860312

Efficient Federated Learning with Self-Regulating Clients

Published in ACM SIGMETRICS / IFIP PERFORMANCE 2022, 2022

Proposed a self regulating clients to save both local computation and communication

Recommended citation: Talukder, Zahidur, and Mohammad A. Islam. "Efficient Federated Learning with Self-Regulating Clients." https://www.researchgate.net/profile/Zahidur-Talukder-2/publication/361939981_FedSRC_Efficient_Federated_Learning_with_Self-Regulating_Clients/links/62cda209c276426014a9677c/FedSRC-Efficient-Federated-Learning-with-Self-Regulating-Clients.pdf