|Original language||English (US)|
|Title of host publication||SoCC 2018 - Proceedings of the 2018 ACM Symposium on Cloud Computing|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||1|
|State||Published - Oct 11 2018|
|Event||2018 ACM Symposium on Cloud Computing, SoCC 2018 - Carlsbad, United States|
Duration: Oct 11 2018 → Oct 13 2018
|Name||SoCC 2018 - Proceedings of the 2018 ACM Symposium on Cloud Computing|
|Other||2018 ACM Symposium on Cloud Computing, SoCC 2018|
|Period||10/11/18 → 10/13/18|
Bibliographical noteFunding Information:
However, the size of parameters shared every iteration is fixed across machines in the existing work since they do not consider heterogeneous networks. Moreover the data size trained in unit time can vary depending on computing power. Thus, it is important that the frequency, size of parameters as well as its contribution to parameter updates should be more precisely and dynamically controlled by the DL framework based on different individual system resources and environments for better model accuracy. Acknowledgement. This work is supported in part by NSF grant III-1422802.
This work is supported in part by NSF grant III-1422802.
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