US 12,393,883 B2
Adaptively synchronizing learning of multiple learning models
Vishesh Garg, Bangalore Karnataka (IN); Sathyanarayanan Manamohan, Bangalore Karnataka (IN); and Krishnaprasad Lingadahalli Shastry, Bangalore Karnataka (IN)
Assigned to Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed by Hewlett Packard Enterprise Development LP, Houston, TX (US)
Filed on Dec. 15, 2020, as Appl. No. 17/122,475.
Claims priority of application No. 202041004375 (IN), filed on Jan. 31, 2020.
Prior Publication US 2021/0241183 A1, Aug. 5, 2021
Int. Cl. G06N 20/20 (2019.01); G06F 9/38 (2018.01); G06F 17/18 (2006.01)
CPC G06N 20/20 (2019.01) [G06F 9/3891 (2013.01); G06F 17/18 (2013.01)] 20 Claims
OG exemplary drawing
 
6. A method, comprising:
periodically receiving, at a master node of a plurality of data processing nodes, a set of learning model weights from each node of the plurality of data processing nodes,
wherein the each successive set of learning model weights from the respective nodes are iteratively received after a predefined synchronization interval, wherein the predefined synchronization interval is stored as an integer value at the master node, and wherein the set of learning model weights are received for a predefined number of iterations;
aggregating, after each of the predefined number of iterations, the set of learning model weights received after the predefined synchronization interval;
generating central learning models based on the aggregated set of learning model weights received after each of the predefined number of iterations;
generating an average accuracy value associated with the central learning models;
determining a difference between an accuracy of a central learning model and the average accuracy value;
comparing the difference in accuracy to a set of thresholds;
when the difference in accuracy is outside of a range established by the set of thresholds, downscaling, in accordance with the integer value, the predefined synchronization interval by a predefined scaling factor that increases sharing of the set of learning model weights by the data processing nodes modifying the predefined synchronization interval; and
transmitting, to the plurality of data processing nodes, the modified synchronization interval.