Praneeth Vepakomma
Cited by
Cited by
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 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
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
Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data
P Vepakomma, O Gupta, T Swedish, R Raskar
Detailed comparison of communication efficiency of split learning and federated learning
A Singh, P Vepakomma, O Gupta, R Raskar, 2019
Reducing Leakage In Distributed Deep Learning For Sensitive Health Data
P Vepakomma, O Gupta, D Abhimanyu, R Raskar
ICLR AI for Social Good, 2019
Supervised Dimensionality Reduction via Distance Correlation Maximization
P Vepakomma, C Tonde, A Elgammal
Electronic Journal of Statistics (Journal) 12 (1), 960-984, 2018
(Interviewed in) Data scientist: the definitive guide to becoming a data scientist
Z Voulgaris
Technics Publications, 2014
No Peek: A Survey of private distributed deep learning
P Vepakomma, T Swedish, R Raskar, O Gupta, A Dubey
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
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, 2018
A Review of Homomorphic Encryption Libraries for Secure Computation
SS Sathya, P Vepakomma, R Raskar, R Ramachandra, S Bhattacharya
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
Tristan Swedish, Ramesh Raskar, Otkrist Gupta, and Abhimanyu Dubey. No peek: A survey of private distributed deep learning
P Vepakomma
arXiv preprint arXiv:1812.03288, 2018
Prediction accuracy in a spatio-temporal prediction system
P Vepakomma, E Copp, A Reynolds
US Patent App. 14/480,523, 2015
Assessing disease exposure risk with location data; A proposal for cryptographic preservation of privacy
A Berke, M Bakker, P Vepakomma, R Raskar, K Larson, A Pentland
arXiv preprint arXiv:2003.14412, 2020
Riddhiman Das, Kaushal Jain, Khahlil Louisy, Greg Nadeau, Vitor Pamplona, Steve Penrod, Yasaman Rajaee, Abhishek Singh, Greg Storm, and John Werner
R Raskar, I Schunemann, R Barbar, K Vilcans, J Gray, P Vepakomma, ...
Apps gone rogue: Maintaining personal privacy in an epidemic, 2020
Split Learning for collaborative deep learning in healthcare
MG Poirot, P Vepakomma, K Chang, J Kalpathy-Cramer, R Gupta, ...
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, ...
arXiv preprint arXiv:2007.13518, 2020
Privacy in Deep Learning: A Survey
F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ...
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