7 citations · 17 across the 11 of their papers we have counts for
13 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…
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning
Charuka Herath, Yogachandran Rahulamathavan, Xiaolan Liu
Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to o…
Deep Learning for Encrypted Traffic Classification and Unknown Data Detection
Madushi H. Pathmaperuma, Yogachandran Rahulamathavan, Safak Dogan +2
Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks…
Privacy-preserving Similarity Calculation of Speaker Features Using Fully Homomorphic Encryption
Yogachandran Rahulamathavan
Recent advances in machine learning techniques are enabling Automated Speech Recognition (ASR) more accurate and practical. The evidence of this can be seen in the rising number of…
Scalar Product Lattice Computation for Efficient Privacy-preserving Systems
Yogachandran Rahulamathavan, Safak Dogan, Xiyu Shi +3
Privacy-preserving applications allow users to perform on-line daily actions without leaking sensitive information. Privacy-preserving scalar product is one of the critical algorit…
On Energy Harvesting of Hybrid TDMA-NOMA Systems
Haitham Al-Obiedollah, Kanapathippillai Cumanan, Alister G. Burr +4
In this paper, we investigate energy harvesting capabilities of non-orthogonal multiple access (NOMA) scheme integrated with the conventional time division multiple access (TDMA) s…
User Collusion Avoidance Scheme for Privacy-Preserving Decentralized Key-Policy Attribute-Based Encryption -- Full Version
Yogachandran Rahulamathavan
Recent trend towards cloud computing paradigm, smart devices and 4G wireless technologies has enabled seamless data sharing among users. Cloud computing environment is distributed…
Spontaneous expression classification in the encrypted domain
Segun Aina, Yogachandran Rahulamathavan, Raphael C. -W. Phan +1
To date, most facial expression analysis have been based on posed image databases and is carried out without being able to protect the identity of the subjects whose expressions ar…