All Outputs (2)

Improving federated learning performance with similarity guided feature extraction and pruning. (2024)
Thesis
PALIHAWADANA, C. 2024. Improving federated learning performance with similarity guided feature extraction and pruning. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801100

Federated Learning (FL) is a Machine Learning (ML) paradigm that learns from distributed clients to collaboratively train a global model in a privacy-preserved manner without sharing their private data. Traditional centralised ML approaches require a... Read More about Improving federated learning performance with similarity guided feature extraction and pruning..

Reasoning with counterfactual explanations for code vulnerability detection and correction. (2021)
Presentation / Conference Contribution
WIJEKOON, A. and WIRATUNGA, N. 2021. Reasoning with counterfactual explanations for code vulnerability detection and correction. In Sani, S. and Kalutarage, H. (eds.) AI and cybersecurity 2021: proceedings of the 2021 Workshop on AI and cybersecurity (AI-Cybersec 2021), co-located with the 41st Specialist Group on Artificial Intelligence international conference on artificial intelligence (SGAI 2021), 14 December 2021, [virtual event]. CEUR workshop proceedings, 3125. Aachen: CEUR-WS [online], pages 1-13. Available from: http://ceur-ws.org/Vol-3125/paper1.pdf

Counterfactual explanations highlight "actionable knowledge" which helps the end-users to understand how a machine learning outcome could be changed to a more desirable outcome. In code vulnerability detection, understanding these "actionable" correc... Read More about Reasoning with counterfactual explanations for code vulnerability detection and correction..