354 citations · 833 across the 13 of their papers we have counts for
5 papers · 1 filter
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from…
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
Ramy E. Ali, Jinhyun So, A. Salman Avestimehr
Outsourcing deep neural networks (DNNs) inference tasks to an untrusted cloud raises data privacy and integrity concerns. While there are many techniques to ensure privacy and inte…
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…
Byzantine-Resilient Secure Federated Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved th…
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A maj…