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.

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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