| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and TrendsŪ in Machine Learning 14 (1–2), 1-210, 2021 | 1923 | 2021 |
| Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data P Vepakomma, O Gupta, T Swedish, R Raskar | 187 | 2019 |
| Detailed comparison of communication efficiency of split learning and federated learning A Singh, P Vepakomma, O Gupta, R Raskar https://arxiv.org/pdf/1909.09145.pdf, 2019 | 139* | 2019 |
| A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities P Vepakomma, D De, SK Das, S Bhansali IEEE Body Sensor Networks Conference, 2015 | 122 | 2015 |
| Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, M Zhang, H Wang, X Wang, P Vepakomma, A Singh, ... SpicyFL, NeurIPS 2020, 2020 | 111 | 2020 |
| Apps gone rogue: Maintaining personal privacy in an epidemic R Raskar, I Schunemann, R Barbar, K Vilcans, J Gray, P Vepakomma, ... arXiv preprint arXiv:2003.08567, 2020 | 111 | 2020 |
| Tristan Swedish, and Ramesh Raskar. Split learning for health: Distributed deep learning without sharing raw patient data P Vepakomma, O Gupta arXiv preprint arXiv:1812.00564 1, 2018 | 78 | 2018 |
| No peek: A survey of private distributed deep learning P Vepakomma, T Swedish, R Raskar, O Gupta, A and Dubey arXiv preprint arXiv:1812.03288 8, 2018 | 64 | 2018 |
| Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic A Berke, M Bakker, P Vepakomma, R Raskar, K Larson, AS Pentland | 58* | 2020 |
| Privacy in Deep Learning: A Survey F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ... | 53 | 2020 |
| Reducing Leakage In Distributed Deep Learning For Sensitive Health Data P Vepakomma, O Gupta, D Abhimanyu, R Raskar ICLR AI for Social Good, 2019 | 53 | 2019 |
| Split Learning for collaborative deep learning in healthcare MG Poirot, P Vepakomma, K Chang, J Kalpathy-Cramer, R Gupta, ... | 40 | 2019 |
| Supervised Dimensionality Reduction via Distance Correlation Maximization P Vepakomma, C Tonde, A Elgammal Electronic Journal of Statistics (Journal) 12 (1), 960-984, 2018 | 38 | 2018 |
| A Review of Homomorphic Encryption Libraries for Secure Computation SS Sathya, P Vepakomma, R Raskar, R Ramachandra, S Bhattacharya | 32 | 2018 |
| SplitNN-driven vertical partitioning I Ceballos, V Sharma, E Mugica, A Singh, A Roman, P Vepakomma, ... arXiv preprint arXiv:2008.04137, 2020 | 15 | 2020 |
| A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System P Vepakomma, A Elgammal Applied and Computational Harmonic Analysis, 2016 | 13 | 2016 |
| DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks A Singh, A Chopra, V Sharma, E Garza, E Zhang, P Vepakomma, ... IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021 | 9 | 2021 |
| Data Markets to support AI for All: Pricing, Valuation and Governance R Raskar, P Vepakomma, T Swedish, A Sharan https://arxiv.org/pdf/1905.06462.pdf, 2019 | 7 | 2019 |
| DAMS: Meta-estimation of private sketch data structures for differentially private COVID-19 contact tracing P Vepakomma, SN Pushpita, R Raskar | 5* | |
| ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations V Sharma, P Vepakomma, T Swedish, K Chang, J Kalpathy-Cramer, ... | 3 | 2019 |
Ramesh RaskarAssociate Professor, MIT Media LabVerified email at mit.edu
