Md Akmol Masud

I’m Akmol — Electrical and Computer Engineering at Queen’s University (Kingston, Canada), with research focus on federated learning over wireless networks.

My research sits at the intersection of distributed optimization, split learning, and privacy-preserving ML — with a focus on making collaborative learning work under real-world constraints: heterogeneous devices, limited bandwidth, and non-iid data.

Previously at Jahangirnagar University (B.Sc., ICT), I published work spanning FL, biomedical signal processing, and quantum ML — including papers in TMLR, PLOS ONE, and Biomedical Signal Processing and Control. With that foundation, I’m now investing my time fully in wireless FL.

If you have any discussion, idea, or just want a conversation — feel free to reach out. Always happy to connect.

news

Apr 08, 2026	MosQNet-SA published in PLOS ONE
Apr 01, 2026	Our paper DREAM: A Novel Explainable Neural Network for Detecting Sleep Apnea Using Single-Lead ECG Signals appears in Biomedical Signal Processing and Control (Vol. 114, April 2026; ScienceDirect, DOI).
Feb 15, 2026	Admitted to Queen's M.A.Sc. (ECE) — wireless federated learning with Dr. Ning Lu
Feb 01, 2026	Our paper Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination was published in Transactions on Machine Learning Research (TMLR) (February 2026 issue; OpenReview, arXiv:2411.01642).
Jan 30, 2026	Presented FedDQN-TSR at ICECTE 2026 (RUET)

selected publications

TMLR
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination

Md Abrar Jahin, Md Akmol Masud, M. F. Mridha, and 1 more author

Transactions on Machine Learning Research, 2026

Abs arXiv Bib HTML

We propose a quantum rationale-aware graph contrastive learning framework for jet discrimination in high-energy physics, combining graph neural networks with quantum computing to improve classification performance.
@article{masud2026quantumcontrastive, author = {Jahin, Md Abrar and Masud, Md Akmol and Mridha, M. F. and Dey, Nilanjan}, title = {Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination}, journal = {Transactions on Machine Learning Research}, year = {2026}, url = {https://arxiv.org/abs/2411.01642}, }
arXiv
Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select

Md Akmol Masud, Md Abrar Jahin, and Mahmud Hasan

2025

Abs arXiv Bib HTML

HeteRo-Select is a client selection framework for federated learning under extreme statistical and system heterogeneity, featuring variance-aware scheduling and heterogeneity-aware aggregation strategies.
@misc{masud2025heteroselect, author = {Masud, Md Akmol and Jahin, Md Abrar and Hasan, Mahmud}, title = {Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select}, year = {2025}, url = {https://arxiv.org/abs/2508.06692}, }

PLOS ONE

MosQNet-SA: Explainable Convolutional-Attention Network for Mosquito Classification with Application as a RESTful API for Dengue and Malaria Risk Mapping

Md Akmol Masud, Sanjida Akter, Nadia Sultana, and 4 more authors

PLOS ONE, 2026

Abs DOI Bib HTML

@article{masud2026mosquito,
  author = {Masud, Md Akmol and Akter, Sanjida and Sultana, Nadia and Islam, Mohammad Shahidul and Abu Yousuf, Mohammed and Noori, Farzan M. and Uddin, Md Zia},
  title = {MosQNet-SA: Explainable Convolutional-Attention Network for Mosquito Classification with Application as a RESTful API for Dengue and Malaria Risk Mapping},
  journal = {PLOS ONE},
  year = {2026},
  volume = {21},
  number = {4},
  pages = {1--30},
  doi = {10.1371/journal.pone.0344970},
  url = {https://doi.org/10.1371/journal.pone.0344970},
}