25 citations · 36 across the 2 of their papers we have counts for
6 papers
Sustainable Federated Learning
Basak Guler, Aylin Yener
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introd…
Energy-Harvesting Distributed Machine Learning
Basak Guler, Aylin Yener
This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machi…
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…
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…
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML ke…