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 001; Atal Narayan Sahu; Marco Canini; Panos Kalnis (AAAI, 2020)

GRACE: A Compressed Communication Framework for Distributed Machine Learning. - Hang Xu; Chen-Yu Ho 001; Ahmed M. Abdelmoniem; Aritra Dutta; El Houcine Bergou; Konstantinos Karatsenidis; Marco Canini; Panos Kalnis (ICDCS, 2021)

Scaling Distributed Machine Learning with In-Network Aggregation. - Amedeo Sapio; Marco Canini; Chen-Yu Ho 001; Jacob Nelson 001; Panos Kalnis; Changhoon Kim; Arvind Krishnamurthy; Masoud Moshref; Dan R. K. Ports; Peter Richtárik (NSDI, 2021)

Efficient sparse collective communication and its application to accelerate distributed deep learning. - Jiawei Fei; Chen-Yu Ho 001; Atal Narayan Sahu; Marco Canini; Amedeo Sapio (SIGCOMM, 2021)

On the Impact of Device and Behavioral Heterogeneity in Federated Learning. - Ahmed M. Abdelmoniem; Chen-Yu Ho 001; Pantelis Papageorgiou; Muhammad Bilal 007; Marco Canini (CoRR, 2021)

Empirical analysis of federated learning in heterogeneous environments. - Ahmed M. Abdelmoniem; Chen-Yu Ho 001; Pantelis Papageorgiou; Marco Canini (EuroMLSys@EuroSys, 2022)

Natural Compression for Distributed Deep Learning. - Samuel Horváth; Chen-Yu Ho 001; Ludovit Horvath; Atal Narayan Sahu; Marco Canini; Peter Richtárik (MSML, 2022)

A Comprehensive Empirical Study of Heterogeneity in Federated Learning. - Ahmed M. Abdelmoniem; Chen-Yu Ho 001; Pantelis Papageorgiou; Marco Canini (IEEE Internet Things J., 2023)