Stebėti
Praneeth Vepakomma
Pavadinimas
Cituota
Cituota
Metai
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and trends® in machine learning 14 (1–2), 1-210, 2021
54412021
Split Learning for Health: Distributed Deep Learning Without Sharing Raw Patient Data
P Vepakomma, O Gupta, T Swedish, R Raskar
6062019
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
3872020
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
381*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
1702015
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
1362020
Privacy in Deep Learning: A Survey
F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar, ...
1342020
Split Learning for collaborative deep learning in healthcare
MG Poirot, P Vepakomma, K Chang, J Kalpathy-Cramer, R Gupta, ...
1322019
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
1192018
Reducing Leakage In Distributed Deep Learning For Sensitive Health Data
P Vepakomma, O Gupta, D Abhimanyu, R Raskar
ICLR AI for Social Good, 2019
952019
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
80*2020
Splitnn-driven vertical partitioning
I Ceballos, V Sharma, E Mugica, A Singh, A Roman, P Vepakomma, ...
arXiv preprint arXiv:2008.04137, 2020
592020
Tristan Swedish, and Ramesh Raskar. 2018. Split learning for health: Distributed deep learning without sharing raw patient data
P Vepakomma, O Gupta
arXiv preprint arXiv:1812.00564, 2018
582018
A Review of Homomorphic Encryption Libraries for Secure Computation
SS Sathya, P Vepakomma, R Raskar, R Ramachandra, S Bhattacharya
572018
Supervised Dimensionality Reduction via Distance Correlation Maximization
P Vepakomma, C Tonde, A Elgammal
Electronic Journal of Statistics (Journal) 12 (1), 960-984, 2018
482018
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
342021
LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning
S Oh, J Park, P Vepakomma, S Baek, R Raskar, ...
The Web Conference, (WWW 2022), 2022
282022
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
S Pal, M Uniyal, J Park, P Vepakomma, R Raskar, M Bennis, M Jeon, ...
arXiv preprint arXiv:2112.05929, 2022
172022
Adasplit: Adaptive trade-offs for resource-constrained distributed deep learning
A Chopra, SK Sahu, A Singh, A Java, P Vepakomma, V Sharma, ...
arXiv preprint arXiv:2112.01637, 2021
172021
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
152016
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Straipsniai 1–20