Projects - SAIL

Active Projects

Current lines of work across trustworthy AI, distributed learning, and efficient machine learning.

Distributed Learning / Trustworthy AI 2026–2028

TrustFed: Trustworthy Federated Large Language Models

A research project on trustworthy federated learning for large language models, focusing on robustness, privacy, evaluation, and scalable collaboration.

Funded by the Accelerating Research Excellence Program, VinUniversity. Principal Investigator: Prof. Kok-Seng Wong.

Federated learningLarge language modelsTrustworthy AIPrivacyRobustness

Trustworthy AI / Distributed Learning

Privacy-Preserving, Robust, and Explainable Federated Learning for Healthcare

Federated learning methods for healthcare systems where privacy, robustness, and interpretability are central requirements.

Healthcare AIPrivacyRobustnessExplainabilityCross-silo learning

Efficient Machine Learning

Green Serverless Computing for Resource-Efficient AI Training

Resource-efficient AI training infrastructure with an emphasis on greener, scalable serverless computing.

Green AIResource efficiencyServerless computingEfficient training

Trustworthy AI / Distributed Learning

Robust Federated Learning under Backdoor Threats

Benchmarking, understanding, and improving the robustness of federated learning systems under adversarial conditions.

Federated learningRobustnessBackdoor attacks and defensesBenchmarking

Efficient Machine Learning / Distributed Learning

Efficient Federated Learning on Edge Devices

Federated learning methods for edge and IoT environments where memory, communication, and compute are constrained.

Edge AICommunication efficiencyResource-constrained learningClient heterogeneity

Completed Projects

Completed projects remain visible to show the lab research history.

Trustworthy AI 2021–2023

Privacy-Preserving Data Publishing for Autonomous Vehicles

Privacy-preserving data publishing methods for autonomous vehicle systems and mobility data.

PrivacyAutonomous systemsData publishingTrust