- Patent Title: Privacy-aware model generation for hybrid machine learning systems
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Application No.: US15996645Application Date: 2018-06-04
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Publication No.: US10536344B2Publication Date: 2020-01-14
- Inventor: Grégory Mermoud , Jean-Philippe Vasseur , Andrea Di Pietro , Erwan Barry Tarik Zerhouni
- Applicant: Cisco Technology, Inc.
- Applicant Address: US CA San Jose
- Assignee: Cisco Technology, Inc.
- Current Assignee: Cisco Technology, Inc.
- Current Assignee Address: US CA San Jose
- Agency: Behmke Innovation Group LLC
- Agent James Behmke; Stephen D. LeBarron
- Main IPC: H04L12/24
- IPC: H04L12/24 ; G06F21/62 ; H04L12/26 ; G06N20/00

Abstract:
In one embodiment, a network assurance service executing in a local network clusters measurements obtained from the local network regarding a plurality of devices in the local network into measurement clusters. The network assurance service computes aggregated metrics for each of the measurement clusters. The network assurance service sends a machine learning model computation request to a remote service outside of the local network that includes the aggregated metrics for each of the measurement clusters. The remote service uses the aggregated metrics to train a machine learning-based model to analyze the local network. The network assurance service receives the trained machine learning-based model to analyze performance of the local network. The network assurance service uses the receive machine learning-based model to analyze performance of the local network.
Public/Granted literature
- US20190372859A1 PRIVACY-AWARE MODEL GENERATION FOR HYBRID MACHINE LEARNING SYSTEMS Public/Granted day:2019-12-05
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