Project

Split Learning: Distributed and collaborative learning

https://splitlearning.github.io/

Groups

Publication

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

IEEE Global Communications Conference (GLOBECOM), 2021

Publication

ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar. In NeurIPS Workshop on Machine learning for the Developing World (ML4D), 2019

Publication

Detailed comparison of communication efficiency of split learning and federated learning,

Praneeth Vepakomma, et al. "Detailed comparison of communication efficiency of split learning and federated learning." arXiv:1909.09145v1 [cs.LG] 18 Sep 2019.

Publication

Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm

Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm, Praneeth Vepakomma, Yulia Kempner

Publication

Reducing leakage in distributed deep learning for sensitive health data

Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, Ramesh Raskar. Reducing Leakage in Distributed Deep Learning for Sensitive Health Data, ICLR 2019 AI for Social Good Workshop (2019).

Publication

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

Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar. Split learning for health: Distributed deep learning without sharing raw patient data, arXiv.org, ICLR 2019 AI for Social Good Workshop (2018).