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)

On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems. - Atal Narayan Sahu; Aritra Dutta; Aashutosh Tiwari; Peter Richtárik (CoRR, 2020)

Rethinking gradient sparsification as total error minimization. - Atal Narayan Sahu; Aritra Dutta; Ahmed M. Abdelmoniem; Trambak Banerjee; Marco Canini; Panos Kalnis (NeurIPS, 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)

Resource-Efficient Federated Learning. - Ahmed M. Abdelmoniem; Atal Narayan Sahu; Marco Canini; Suhaib A. Fahmy (CoRR, 2021)

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

REFL: Resource-Efficient Federated Learning. - Ahmed M. Abdelmoniem; Atal Narayan Sahu; Marco Canini; Suhaib A. Fahmy (EuroSys, 2023)