7 citations · 9 across the 6 of their papers we have counts for
6 papers
Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
Mindula Illeperuma, Rafael Pina, Charuka Herath +2
Structured pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constrained devices. Popular methods…
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…
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…