WeightGrad: Geo-Distributed Data Analysis Using Quantization for Faster Convergence and Better Accuracy-Reference-Cited by-同舟云学术

WeightGrad: Geo-Distributed Data Analysis Using Quantization for Faster Convergence and Better Accuracy

Published:2020-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
language:
Short-container-title:

Author:

Akter Syeda Nahida¹,Adnan Muhammad Abdullah¹

Affiliation:

1. Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh

Funder

AWS Cloud Credits for Research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403097

Reference42 articles.

1. [n.d.]. AWS. http://aws.amazon.com/about-aws/global-infrastructure/. [n.d.]. AWS. http://aws.amazon.com/about-aws/global-infrastructure/.

2. [n.d.]. AWS EC2 Instance. https://aws.amazon.com/ec2/instance-types/. [n.d.]. AWS EC2 Instance. https://aws.amazon.com/ec2/instance-types/.

3. [n.d.]. Google Datacenter Locations. http://www.google.com/about/datacenters/inside/locations/. [n.d.]. Google Datacenter Locations. http://www.google.com/about/datacenters/inside/locations/.

4. [n.d.]. isperf3. https://software.es.net/iperf/. [n.d.]. isperf3. https://software.es.net/iperf/.

5. [n.d.]. Microsoft Datacenters. http://www.microsoft.com/en-us/server-cloud/cloud-os/global-datacenters.aspx. [n.d.]. Microsoft Datacenters. http://www.microsoft.com/en-us/server-cloud/cloud-os/global-datacenters.aspx.

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