Publications

You can also find my articles on my Google Scholar profile.

2026

Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon

J. Mu et al.

BioHackrXiv

This preprint presents prototype federated learning software developed during the 2026 Carnegie Mellon University–NVIDIA Federated Learning Hackathon for Biomedical Applications. The work demonstrates federated frameworks across biomedical tasks including disease subtyping, genetic association studies, histopathology harmonization, rare disease stratification, cancer subtyping, polygenic risk score aggregation, and multimodal clinical prediction.

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FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning

A. Devkota, J. Thrasher, D. Adjeroh, B. Bhattarai, P. K. Gyawali

CVPR Findings 2026

This study introduces FedVG, a gradient-guided federated aggregation framework designed to improve learning under client data heterogeneity. By using a global validation set and layerwise validation gradient norms, FedVG adaptively weights client models based on their generalization behavior, improving performance across natural and medical image benchmarks, especially in highly heterogeneous settings.

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Local K-Similarity Constraint for Federated Learning with Label Noise

S. Amgain, P. Shrestha, B. Khanal, A. Devkota, Y. R. Shrestha, S. Baek, P. Gyawali, B. Bhattarai

IEEE International Symposium on Biomedical Imaging (ISBI) 2026

This study introduces a local regularization objective for federated learning with noisy labels. The method uses the representation space of a self-supervised pretrained model to enforce similarity between nearby examples within each client, improving robustness in heterogeneous federated settings with many noisy clients.

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2025

Federated Foundation Model for GI Endoscopy Images

A. Devkota, A. Amireskandari, J. Palko, S. Thakkar, D. Adjeroh, X. Jiang, B. Bhattarai, P. K. Gyawali

Under review, Npj Digital Medicine

We propose a federated learning framework to train foundation models for gastrointestinal endoscopy imaging, allowing hospitals to collaboratively develop general-purpose models while keeping data private. Our approach is evaluated on classification, detection, and segmentation tasks, demonstrating improved performance in a privacy-preserving, federated setting.

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AI analysis for ejection fraction estimation from 12-lead ECG

A. Devkota, R. Prajapati, A. El-Wakeel, D. Adjeroh, B. Patel, P. Gyawali

Scientific Reports

This study investigates the use of 12-lead ECG signals to estimate heart ejection fraction (EF) in a rural Appalachian population. Using a range of machine learning and deep learning models—including Transformers—our analysis shows deep learning models achieve the highest performance (AUROC ~0.86), with specific multi-lead combinations improving accuracy and model interpretability providing insights into predictive features.

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Multimodal Federated Learning for Secure and Accurate Healthcare AI

J. Thrasher, A. Devkota, P. Siwakotai, R. Chivukula, P. Poudel, C. Hu, B. Bhattarai, P. Gyawali

Journal of Healthcare Informatics Research

This paper reviews the role of multimodal federated learning in healthcare, highlighting its potential to combine diverse medical data while preserving patient privacy. It surveys state-of-the-art approaches, identifies current challenges and limitations, and outlines future directions for advancing secure and effective healthcare AI.

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2024

TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis

J. Thrasher, A. Devkota, A. P. Tafti, B. Bhattarai, P. Gyawali, Alzheimer’s Disease Neuroimaging Initiative

MICCAI 2024

We introduce TE-SSL, a time and event-aware self-supervised learning framework for Alzheimer’s disease progression analysis. By incorporating time-to-event and event data as supervisory signals, TE-SSL improves representation learning and outperforms existing SSL methods in downstream survival analysis tasks.

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2021

Near Real-Time Mobile Profiling and Modeling of Fine-Scale Environmental Proxies Along Major Road Lines of Nepal

N. B. Adhikari, S. Gautam, A. Devkota, S. Shikha, S. Pyakurel, M. P. Adhikari

ICMSI

We present a methodology using GPS-enabled mobile sensors to collect and model fine-scale environmental proxies (e.g., temperature, CO₂, PM₂.₅) along major roadways in Nepal, demonstrating the effectiveness of ARIMA and RNNs for real-time and historical climate modeling.

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2018

Sentence Ranking and Answer Pinpointing in Online Discussion Forums Utilizing User-generated Metrics and Highlights

S. Gautam, S. Shikha, A. Devkota, S. Pyakurel

NASCOIT

This work presents a framework for extracting precise answers from online discussion forums by combining sentence ranking with user-generated metrics and highlights. The approach enables accurate answer pinpointing, improving search relevance and supporting better question-answering in forums.

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