Chen-Yu Ho

According to our database, Chen-Yu Ho authored at least 8 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

A Comprehensive Empirical Study of Heterogeneity in Federated Learning.

Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Marco Canini

IEEE Internet Things J., August, 2023

2022

Natural Compression for Distributed Deep Learning.

Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Empirical analysis of federated learning in heterogeneous environments.

Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Marco Canini

Proceedings of the EuroMLSys '22: Proceedings of the 2nd European Workshop on Machine Learning and Systems, Rennes, France, April 5, 2022

2021

On the Impact of Device and Behavioral Heterogeneity in Federated Learning.

Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Muhammad Bilal, Marco Canini

CoRR, 2021

Efficient sparse collective communication and its application to accelerate distributed deep learning.

Jiawei Fei, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Amedeo Sapio

Proceedings of the ACM SIGCOMM 2021 Conference, Virtual Event, USA, August 23-27, 2021., 2021

Scaling Distributed Machine Learning with In-Network Aggregation.

Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik

Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, 2021

GRACE: A Compressed Communication Framework for Distributed Machine Learning.

Hang Xu, Chen-Yu Ho, Ahmed M. Abdelmoniem, Aritra Dutta, El Houcine Bergou, Konstantinos Karatsenidis, Marco Canini, Panos Kalnis

Proceedings of the 41st IEEE International Conference on Distributed Computing Systems, 2021

2020

On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning.

Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Panos Kalnis

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020