Publications

Samuel Horváth, Chen-Yu Ho, Ľudovít Horváth, Atal Narayan Sahu, Marco Canini, Peter Richtárik (2022). Natural Compression for Distributed Deep Learning. Proceedings of MSML'22.

Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Marco Canini (2022). Empirical analysis of federated learning in heterogeneous environments. Proceedings of EuroMLSys'22.

Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik (2021). Scaling Distributed Machine Learning with In-Network Aggregation. Proceedings of NSDI'21.

Jiawei Fei, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Amedeo Sapio (2020). Efficient Sparse Collective Communication and its application to Accelerate Distributed Deep Learning. Proceedings of SIGCOMM'21.

Hang Xu, Chen-Yu Ho, Ahmed M. Abdelmoniem, Aritra Dutta, El Houcine Bergou, Konstantinos Karatsenidis, Marco Canini, Panos Kalnis (2020). Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation. Proceedings of ICDCS'21.

Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Panos Kalnis (2020). On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning. Proceedings of AAAI'20.