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Praneeth Vepakomma
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Dec 20, 2020
Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar

* Extended version of NeurIPS PPML 2020 workshop paper 
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NoPeek: Information leakage reduction to share activations in distributed deep learning

Aug 20, 2020
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar


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SplitNN-driven Vertical Partitioning

Aug 07, 2020
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar

* First version, please provide feedback 
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FedML: A Research Library and Benchmark for Federated Machine Learning

Jul 27, 2020
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr

* We maintain the source code, documents, and user community at https://fedml.ai 
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Splintering with distributions: A stochastic decoy scheme for private computation

Jul 07, 2020
Praneeth Vepakomma, Julia Balla, Ramesh Raskar

* 28 pages, 6 figures 
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Privacy in Deep Learning: A Survey

May 09, 2020
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh


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Split Learning for collaborative deep learning in healthcare

Dec 27, 2019
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar

* Workshop paper: 8 pages, 2 figures, 1 table 
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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, AurΓ©lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, AdriΓ  GascΓ³n, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub KonečnΓ½, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, TancrΓ¨de Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Γ–zgΓΌr, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian TramΓ¨r, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao


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ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

Oct 09, 2019
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar

* In NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019 
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ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

Oct 05, 2019
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar

* In NeurIPS Workshop on Machine learning for the Developing World (ML4D) 
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Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest

Sep 27, 2019
Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar


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Detailed comparison of communication efficiency of split learning and federated learning

Sep 18, 2019
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar


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Data Markets to support AI for All: Pricing, Valuation and Governance

May 14, 2019
Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan

* 7 pages, 2 figures 
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No Peek: A Survey of private distributed deep learning

Dec 08, 2018
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey

* 21 pages 
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Split learning for health: Distributed deep learning without sharing raw patient data

Dec 03, 2018
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar


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DISCOMAX: A Proximity-Preserving Distance Correlation Maximization Algorithm

Feb 17, 2017
Praneeth Vepakomma, Ahmed Elgammal

* Withdrawing as an updated and enhanced version of this paper is on arxiv under my name as well titled Supervised Dimensionality Reduction via Distance Correlation Maximization. See arXiv:1601.00236. That makes this version pointless 
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Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data

Dec 28, 2016
Susovan Pal, Praneeth Vepakomma

* 19 pages 
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Supervised Dimensionality Reduction via Distance Correlation Maximization

Jan 03, 2016
Praneeth Vepakomma, Chetan Tonde, Ahmed Elgammal

* 23 pages, 6 figures 
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