6 papers
Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA
Yogachandran Rahulamathavan, Nasir Iqbal, Juncheng Hu +1
Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on everything else. This setting b…
QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva +1
Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequen…
DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregation
Charuka Herath, Yogachandran Rahulamathavan, Varuna De Silva +1
Federated Learning (FL) enables decentralized model training without sharing raw data, offering strong privacy guarantees. However, existing FL protocols struggle to defend against…
PLEX: Perturbation-free Local Explanations for LLM-Based Text Classification
Yogachandran Rahulamathavan, Misbah Farooq, Varuna De Silva
Large Language Models (LLMs) excel in text classification, but their complexity hinders interpretability, making it difficult to understand the reasoning behind their predictions.…
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection
Charuka Herath, Xiaolan Liu, Sangarapillai Lambotharan +1
Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, securi…
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
Yogachandran Rahulamathavan, Charuka Herath, Xiaolan Liu +2
The federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm. While FL ensures that a user's data always remain…