82 citations · 220 across the 34 of their papers we have counts for
16 papers · 1 filter
Private measurement of nonlinear correlations between data hosted across multiple parties
Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar
We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities. We provide utility guarantees of our private estim…
Can Self Reported Symptoms Predict Daily COVID-19 Cases?
Parth Patwa, Viswanatha Reddy, Rohan Sukumaran +6
The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal ac…
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms
Rohan Sukumaran, Parth Patwa, T V Sethuraman +9
It is crucial for policymakers to understand the community prevalence of COVID-19 so combative resources can be effectively allocated and prioritized during the COVID-19 pandemic.…
NoPeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta +1
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensit…
SplitNN-driven Vertical Partitioning
Iker Ceballos, Vivek Sharma, Eduardo Mugica +4
In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distrib…
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsist…