The School of Computer Science is pleased to present…
Fed-ensemble: Enhancing Federated Learning with Ensemble Models for an Explainable Thyroid Cancer Recurrence Prediction
MSc Thesis Defense by: Hasibul Hasan Sabuj
Date: Monday, August 18, 2025
Time: 10:00 AM
Location: Essex Hall, Room 122
The prediction of thyroid cancer recurrence is a critical task in clinical decision-making, yet traditional machine learning models face significant challenges, particularly around data privacy, model generalization, and interpretability. In healthcare, patient data is sensitive and sharing it across institutions for model training raises privacy concerns. This research addresses these issues by utilizing federated learning (FL), a decentralized machine learning approach that allows institutions to collaboratively train a model while ensuring patient data remains private. FL enables local model training at each institution, with only model updates shared across participants, safeguarding sensitive data. Alongside federated learning, the study incorporates Explainable AI (XAI) techniques to enhance the transparency of predictions, enabling clinicians to interpret and trust the model’s decision-making process. By combining multiple machine learning models in an ensemble approach, the research improves the prediction accuracy and robustness of thyroid cancer recurrence, even with limited data. The method is evaluated using a cohort dataset of thyroid cancer patients, with synthetic data augmentation addressing data scarcity. The results demonstrate that the approach outperforms traditional models while addressing critical challenges of data privacy and model interpretability. This work underscores the potential of federated learning and XAI in creating secure, transparent, and effective healthcare solutions for sensitive applications.
Keywords: Federated Learning, Ensemble Models, Thyroid Cancer, Generative Adversarial Network, Explainable AI
Internal Reader: Dr. Alioune Ngom
External Reader: Dr. Abdul A. Hussein
Advisor: Dr. Dan Wu
Chair: Dr. Muhammad Asaduzzaman
