Improving the Utility of Differentially Private SGD by Employing Wavelet Transforms. - Kanishka Ranaweera; David B. Smith 001; Dinh C. Nguyen; Pubudu N. Pathirana; Ming Ding 001; Thierry Rakotoarivelo; Aruna Seneviratne (IEEE Big Data, 2023)

Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data. - Kanishka Ranaweera; Pubudu N. Pathirana (EMBC, 2024)

Optimizing Diagnosis in Sparse Data Environments: A Model Agnostic Meta Learning Approach. - Kanishka Ranaweera; Pubudu N. Pathirana (EMBC, 2024)

Federated Learning with Differential Privacy: An Utility-Enhanced Approach. - Kanishka Ranaweera; Dinh C. Nguyen; Pubudu N. Pathirana; David B. Smith 001; Ming Ding 001; Thierry Rakotoarivelo; Aruna Seneviratne (CoRR, 2025)

Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning. - Kanishka Ranaweera; David B. Smith 001; Pubudu N. Pathirana; Ming Ding 001; Thierry Rakotoarivelo; Aruna Seneviratne (CoRR, 2025)

Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data. - Kanishka Ranaweera; Dinh C. Nguyen; Pubudu N. Pathirana; David B. Smith 001; Ming Ding 001; Thierry Rakotoarivelo; Aruna Seneviratne (CoRR, 2025)

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation. - Kanishka Ranaweera; Azadeh Ghari Neiat; Xiao Liu 004; Bipasha Kashyap; Pubudu N. Pathirana (CoRR, 2025)