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My recent focus is on using machine learning for systems, and building systems for machine learning. One key example is our project on Scalable Federated Learning (SFL). This project aims to develop a highly scalable, flexible, extensible, distributed federated machine learning approach that can directly benefit public health and wellness. More details are available in the SFL blog. In our recent CFD paper we demonstrate our ability to perform dropout on image recognition models using coding theory (e.g., using Gold codes), resulting in more than 2X network bandwidth savings when used for Federated Learning. Our DeepGANTT IPSN '23 paper demonstrates our ability to apply transformers to graph neural networks for scaling up an IoT scheduling problem 10X beyond what a constraint optimization solver can solve in a reasonable time.
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论文共 42 篇作者统计合作学者相似作者
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PeerJ. Computer science (2023): e1385-e1385
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Giacomo Verardo,Magnus Boman, Samuel Bruchfeld,Marco Chiesa,Sabine Koch,Gerald Q. Maguire Jr.,Dejan Kostic
CoRR (2023)
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SoCC '15: ACM Symposium on Cloud Computing
Kohala Coast
Hawaii
August, 2015pp.16-29, (2015)
Rui L Aguiar, Achim Authenrieth,Richard Clegg,Didier Colle,Daniel Corujo,Panagiotis Demestichas,Alisa Devlic, Thomas Dietz,Christian Esteve Rothenberg,Panagiotis Georgopoulos,Eduardo Jacob, Pawel Kaczmarek,
ICDS '07 Proceedings of the First International Conference on the Digital Societypp.xii-xii, (2015)
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