Tournament-Based Pretraining to Accelerate Federated Learning-Reference-Cited by-同舟云学术

Tournament-Based Pretraining to Accelerate Federated Learning

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
language:
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Author:

Baughman Matt¹^ORCID,Hudson Nathaniel²^ORCID,Chard Ryan³^ORCID,Bauer Andre¹^ORCID,Foster Ian⁴^ORCID,Chard Kyle²^ORCID

Affiliation:

1. University of Chicago, United States of America

2. University of Chicago, United States of America and Argonne National Laboratory (ANL), United States of America

3. Argonne National Laboratory, United States of America

4. Argonne National Laboratory, United States of America and University of Chicago, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3626089

Reference30 articles.

1. Adaptive Edge-Cloud Environments for Rural AI

2. Monitoring the mammalian fauna of urban areas using remote cameras and citizen science

3. Matt Baughman Nathaniel Hudson Ian Foster and Kyle Chard. 2023. Balancing Federated Learning Trade-Offs for Heterogeneous Environments. In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE 404–407. Matt Baughman Nathaniel Hudson Ian Foster and Kyle Chard. 2023. Balancing Federated Learning Trade-Offs for Heterogeneous Environments. In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE 404–407.

4. Recognition in Terra Incognita

5. Daniel J. Beutel , Taner Topal , Akhil Mathur , Xinchi Qiu , Javier Fernandez-Marques , Yan Gao , Lorenzo Sani , Kwing Hei Li , Titouan Parcollet , Pedro Porto Buarque de Gusmão, and Nicholas D. Lane . 2022 . Flower : A Friendly Federated Learning Research Framework . arxiv:2007.14390 [cs.LG] Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusmão, and Nicholas D. Lane. 2022. Flower: A Friendly Federated Learning Research Framework. arxiv:2007.14390 [cs.LG]