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ORCIDKanishka Ranaweera
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2020 – today
- 2025
[i4]Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana, David B. Smith, Ming Ding, Thierry Rakotoarivelo, Aruna Seneviratne:
Federated Learning with Differential Privacy: An Utility-Enhanced Approach. CoRR abs/2503.21154 (2025)- [i3]Kanishka Ranaweera, David B. Smith, Pubudu N. Pathirana, Ming Ding, Thierry Rakotoarivelo, Aruna Seneviratne:
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning. CoRR abs/2503.21159 (2025) - [i2]Kanishka Ranaweera, Dinh C. Nguyen, Pubudu N. Pathirana, David B. Smith, Ming Ding, Thierry Rakotoarivelo, Aruna Seneviratne:
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data. CoRR abs/2503.22749 (2025) - [i1]Kanishka Ranaweera, Azadeh Ghari Neiat, Xiao Liu, Bipasha Kashyap, Pubudu N. Pathirana:
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation. CoRR abs/2503.22971 (2025) - 2024
- [c3]Kanishka Ranaweera, Pubudu N. Pathirana:
Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data. EMBC 2024: 1-4 - [c2]Kanishka Ranaweera, Pubudu N. Pathirana:
Optimizing Diagnosis in Sparse Data Environments: A Model Agnostic Meta Learning Approach. EMBC 2024: 1-4 - 2023
- [c1]Kanishka Ranaweera, David B. Smith, Dinh C. Nguyen
, Pubudu N. Pathirana, Ming Ding, Thierry Rakotoarivelo, Aruna Seneviratne:
Improving the Utility of Differentially Private SGD by Employing Wavelet Transforms. IEEE Big Data 2023: 1352-1359
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