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FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horvath · Stefanos Laskaridis · Ilias Leontiadis

Author Information

Samuel Horvath (KAUST)
Samuel Horvath

I am currently an assistant professor of Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to that, I completed my MS and Ph. D. in statistics at King Abdullah University of Science and Technology (KAUST), advised by professor Peter Richtárik. Before that, I was an undergraduate student in Financial Mathematics at Comenius University. In my research, I focus on providing a fundamental understanding of how distributed and federated training algorithms work and how they interact with different sources of heterogeneity, such as system-level variability in the computing infrastructure and statistical variability in the training data. Inspired by the theoretical insights, I aim to design efficient and practical distributed/federated training algorithms. I am broadly interested in federated learning, distributed optimization, and efficient deep learning.

Stefanos Laskaridis (Samsung AI Center Cambridge)
Ilias Leontiadis (Samsung AI)

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