US 2022/0101178 A1 · ADAPTIVE DISTRIBUTED LEARNING MODEL OPTIMIZATION FOR PERFORMANCE PREDICTION UNDER DATA PRIVACY CONSTRAINTS

Abstract

An adaptive distributed learning model optimization for performance prediction under data privacy constraints. Specifically, the disclosed method and system introduce a framework through which a shared machine learning model deployed across a network of computing nodes may be optimized using private and decentralized datasets. Through the proposed framework, the shared machine learning model may achieve a good generalization error globally across the network, and may also achieving good predictive performance locally while employed on each computing node.

Claims20

Dependent claims

A method for adaptive distributed learning model optimization, comprising:

receiving, by a worker node and from a central node, a first learning model configured with an initial learning state;
making a first determination that a first data shift has transpired;
issuing, based on the first determination, a first data shift notice to the central node;
receiving, in response to issuing the first data shift notice, a first data shift instruction from the central node;
and
adjusting, based on the first data shift instruction, the initial learning state through optimization of the first learning model using local data to obtain a second learning model configured with local data adjusted learning state.

3 more elements

A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor on a worker node, enables the computer processor to:

receive, from a central node, a first learning model configured with an initial learning state;
make a first determination that a first data shift has transpired;
issue, based on the first determination, a first data shift notice to the central node;
receive, in response to issuing the first data shift notice, a first data shift instruction from the central node;
and
adjust, based on the first data shift instruction, the initial learning state through optimization of the first learning model using local data to obtain a second learning model configured with local data adjusted learning state.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application

17/032,515Filed 25 Sep 2020

Published

31 Mar 2022

Term ends

Not estimatedThe source records don’t support an estimate.

Classification13 codes

G06N 20/00
Machine learning
G06F 11/3409
Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of…:…
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06F 18/2148
Generating training patterns; Bootstrap methods, e.g. bagging or boosting: characterised by the process organisation or structure, e.g. boosting…

Adaptive distributed learning model optimization for performance prediction under data privacy constraints

Life of this patent

Adaptive distributed learning model optimization for performance prediction under data privacy constraints

Life of this patent

Abstract

Claims20