Praneeth Vepakomma

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 52

Top Topics

Supervised Dimensionality Reduction

Vertical Partitioning

Differentially Private

Top Venues

Publications

Seungeun Oh, Hyelin Nam, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning. IEEE Trans. Big Data 10 (3) (2024)
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR (2024)
Ibrahim Suat Evren, Praneeth Vepakomma
Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems. FORC (2024)
Zheng Lin, Xuanjie Hu, Yuxin Zhang, Zhe Chen, Zihan Fang, Xianhao Chen, Ang Li, Praneeth Vepakomma, Yue Gao
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models. CoRR (2024)
Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images. CoRR (2024)
Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. Trans. Mach. Learn. Res. 2023 (2023)
Praneeth Vepakomma, Yulia Kempner, Rodmy Paredes Alfaro, Ramesh Raskar
Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity. HPEC (2023)
Abhishek Singh, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release. NeurIPS (2023)
Praneeth Vepakomma, Julia Balla, Ramesh Raskar
PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval. AAAI (2022)
Gharib Gharibi, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Zachi Attia, Riddhiman Das, Suraj Kapa, Ramesh Raskar
Blind Inference: An Automated Privacy-Preserving Prediction Service using Secure Multi-Party Computation for Medical Applications. AMIA (2022)
Sihun Baek, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning. CoRR (2022)
Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland
Private independence testing across two parties. CoRR (2022)
Gharib Gharibi, Ravi Patel, Anissa Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das
An Automated Framework for Distributed Deep Learning-A Tool Demo. ICDCS (2022)
Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
Differentially Private CutMix for Split Learning with Vision Transformer. CoRR (2022)
Ibrahim Suat Evren, Praneeth Vepakomma, Ramesh Raskar
The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence & Welfare in Social Choice. CoRR (2022)
Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
Decouple-and-Sample: Protecting sensitive information in task agnostic data release. CoRR (2022)
Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release. ECCV (13) (2022)
Seungeun Oh, Jihong Park, Praneeth Vepakomma, Sihun Baek, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning. WWW (2022)
Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. GLOBECOM (2021)
Praneeth Vepakomma, Julia Balla, Ramesh Raskar
Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval. CoRR (2021)
Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. CoRR (2021)
Abhishek Singh, Ayush Chopra, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks. CVPR (2021)
Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar
Private measurement of nonlinear correlations between data hosted across multiple parties. CoRR (2021)
Shraman Pal, Mansi Uniyal, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Moongu Jeon, Jinho Choi
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning. CoRR (2021)
Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity. CoRR (2021)
Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning. CoRR (2021)
Praneeth Vepakomma, Abhishek Singh, Emily Zhang, Otkrist Gupta, Ramesh Raskar
NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training. FG (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar
NoPeek: Information leakage reduction to share activations in distributed deep learning. ICDM (Workshops) (2020)
Ramesh Raskar, Isabel Schunemann, Rachel Barbar, Kristen Vilcans, Jim Gray, Praneeth Vepakomma, Suraj Kapa, Andrea Nuzzo, Rajiv Gupta, Alex Berke, Dazza Greenwood, Christian Keegan, Shriank Kanaparti, Robson Beaudry, David Stansbury, Beatriz Botero Arcila, Rishank Kanaparti, Vitor F. Pamplona, Francesco M. Benedetti, Alina Clough, Riddhiman Das, Kaushal Jain, Khahlil Louisy, Greg Nadeau, Vitor Pamplona, Steve Penrod, Yasaman Rajaee, Abhishek Singh, Greg Storm, John Werner
Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic. CoRR (2020)
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar
NoPeek: Information leakage reduction to share activations in distributed deep learning. CoRR (2020)
Ramesh Raskar, Greg Nadeau, John Werner, Rachel Barbar, Ashley Mehra, Gabriel Harp, Markus Leopoldseder, Bryan Wilson, Derrick Flakoll, Praneeth Vepakomma, Deepti Pahwa, Robson Beaudry, Emelin Flores, Maciej Popielarz, Akanksha Bhatia, Andrea Nuzzo, Matt Gee, Jay Summet, Rajeev Surati, Bikram Khastgir, Francesco Maria Benedetti, Kristen Vilcans, Sienna Leis, Khahlil Louisy
COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers. CoRR (2020)
Alex Berke, Michiel A. Bakker, Praneeth Vepakomma, Ramesh Raskar, Kent Larson, Alex 'Sandy' Pentland
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic. CoRR (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
FedML: A Research Library and Benchmark for Federated Machine Learning. CoRR (2020)
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
Privacy in Deep Learning: A Survey. CoRR (2020)
Praneeth Vepakomma, Julia Balla, Ramesh Raskar
Splintering with distributions: A stochastic decoy scheme for private computation. CoRR (2020)
Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar
SplitNN-driven Vertical Partitioning. CoRR (2020)
Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks. CoRR (2020)
Priyanka Singh, Abhishek Singh, Gabriel Cojocaru, Praneeth Vepakomma, Ramesh Raskar
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic. CoRR (2020)
Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan
Data Markets to support AI for All: Pricing, Valuation and Governance. CoRR (2019)
Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar
Detailed comparison of communication efficiency of split learning and federated learning. CoRR (2019)
Praneeth Vepakomma, Yulia Kempner
Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm. Discret. Appl. Math. 265 (2019)
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar
Split Learning for collaborative deep learning in healthcare. CoRR (2019)
Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest. CoRR (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries. CoRR (2019)
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations. CoRR (2019)
Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra, Santanu Bhattacharya
A Review of Homomorphic Encryption Libraries for Secure Computation. CoRR (2018)
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey
No Peek: A Survey of private distributed deep learning. CoRR (2018)
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar
Split learning for health: Distributed deep learning without sharing raw patient data. CoRR (2018)
Prashant Shrivastava, Anupam Shukla, Praneeth Vepakomma, Neera Bhansali, Kshitij Verma
A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease. Comput. Methods Programs Biomed. 139 (2017)
Praneeth Vepakomma, Chetan Tonde, Ahmed M. Elgammal
Supervised Dimensionality Reduction via Distance Correlation Maximization. CoRR (2016)
Praneeth Vepakomma, Ahmed M. Elgammal
Iterative Embedding with Robust Correction using Feedback of Error Observed. MLIS@ICML (2015)
Praneeth Vepakomma, Debraj De, Sajal K. Das, Shekhar Bhansali
A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. BSN (2015)