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Zainab Saad, Master Student in Electrical and Software Engineering, DENOS Lab

Zainab Saad

Master Student in Electrical and Software Engineering, University of Calgary

Large Language Model Application, Federated Learning

Biography

Zainab Saad is a Master’s student at DENOS Lab working on large language model applications and federated learning. Her research builds a federated learning framework for carbon-aware container orchestration, predicting workload energy consumption while keeping sensitive operational data inside each enterprise, and she also contributes to the lab’s privacy-preserving federated analysis of clinical trial data.

Projects

  • Carbon-aware container orchestration

    Federated prediction of workload energy consumption so orchestration platforms such as Kubernetes can schedule for carbon without centralizing operational data.

Publications

  • ACM Transactions on Computing for Healthcare2026

    Starfish-FL: Harnessing Agentic Federated Analytics

    Yunkai Bao, Zainab Saad, Farhan Abbas, Kaue Duarte, Tolulope Sajobi, Bijoy Menon, Jiayu Zhou, and Steve Drew

    paper

  • IEEE SWC2025

    Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning

    Zainab Saad, Jialin Yang, Henry Leung, and Steve Drew

    paper

  • arXiv2025

    A Survey on Task Scheduling in Carbon-aware Container Orchestration

    Jialin Yang, Zainab Saad, Jiajun Wu, Xiaoguang Niu, Henry Leung, and Steve Drew

    paper

  • SSRN

    Privacy-Preserving Federated Analysis Reproduces Non-Inferiority Results from the AcT Multicentre Stroke Trial

    Yunkai Bao, Zainab Saad, K. Duarte, Farhan Abbas, T. Sajobi, Jessalyn K. Holodinsky, Bijoy K. Menon, et al.

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