855 citations · 3.9k across the 142 of their papers we have counts for
14 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…
Secure Aggregation with Heterogeneous Quantization in Federated Learning
Ahmed Roushdy Elkordy, A. Salman Avestimehr
Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additiv…
Analog Lagrange Coded Computing
Mahdi Soleymani, Hessam Mahdavifar, A. Salman Avestimehr
A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is disperse…
Privacy-Preserving Distributed Learning in the Analog Domain
Mahdi Soleymani, Hessam Mahdavifar, A. Salman Avestimehr
We consider the critical problem of distributed learning over data while keeping it private from the computational servers. The state-of-the-art approaches to this problem rely on…
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Chaoyang He, Murali Annavaram, Salman Avestimehr
Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on…