Ashkan Yousefpour
Ashkan is a Reseach Scientist at Facebook AI. He is currently working on privacy-preserving machine learning and federated learning at Facebook AI, and is also a contributor to Opacus. Prior to joining Facebook AI, he was a Visiting Researcher at University of California, Berkeley. Ashkan holds a PhD in Computer Science from the University of Texas at Dallas.
Ashkan's Publications
July 24, 2021
CORE MACHINE LEARNING
SYSTEMS RESEARCH
Federated Learning with Buffered Asynchronous Aggregation
Federated Learning (FL) trains a shared model across distributed devices while keeping the training data on the devices. Most FL schemes are synchronous: they perform a synchronized aggregation of model updates from individual devices. …
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, Dzmitry Huba
July 24, 2021
RESEARCH
SYSTEMS RESEARCH
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
Techniques such as Federated Learning and Split Learning aim to train distributed deep learning models without sharing private data. In Split Learning, when a neural network is partitioned and distributed across physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop.
Ashkan Yousefpour, Brian Q. Nguyen, Siddartha Devic, Guanhua Wang, Aboudy Kreidieh, Hans Lobel, Alexandre M. Bayen, Jason P. Jue
