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Praneeth Vepakomma
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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
19342021
Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data
P Vepakomma, O Gupta, T Swedish, R Raskar
1872019
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
140*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
1222015
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
1132020
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
1122020
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
782018
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
642018
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
59*2020
Privacy in Deep Learning: A Survey
F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ...
532020
Reducing Leakage In Distributed Deep Learning For Sensitive Health Data
P Vepakomma, O Gupta, D Abhimanyu, R Raskar
ICLR AI for Social Good, 2019
532019
Split Learning for collaborative deep learning in healthcare
MG Poirot, P Vepakomma, K Chang, J Kalpathy-Cramer, R Gupta, ...
402019
Supervised Dimensionality Reduction via Distance Correlation Maximization
P Vepakomma, C Tonde, A Elgammal
Electronic Journal of Statistics (Journal) 12 (1), 960-984, 2018
382018
A Review of Homomorphic Encryption Libraries for Secure Computation
SS Sathya, P Vepakomma, R Raskar, R Ramachandra, S Bhattacharya
322018
SplitNN-driven vertical partitioning
I Ceballos, V Sharma, E Mugica, A Singh, A Roman, P Vepakomma, ...
arXiv preprint arXiv:2008.04137, 2020
152020
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
132016
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
92021
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
72019
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, ...
32019
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