公开(公告)号：US20250138794A1

公开(公告)日：2025-05-01

申请号：US18385555

申请日：2023-10-31

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Ramana Rao V. R. KOMPELLA

IPC: G06F8/41

Abstract: In one implementation, a method is disclosed comprising: identifying, by a device, a plurality of functions within a source code based on one or more programmatic annotations of each of the plurality of functions within the source code; monitoring, by the device, execution characteristics associated with each of the plurality of functions within the source code during execution; constructing, by the device, a function call graph from the plurality of functions wherein each particular function in the function call graph is annotated with corresponding execution characteristics; and partitioning, by the device and based on the function call graph and one or more deployment specifications, the plurality of functions within the source code into singularly executable function capsules that meet the one or more deployment specifications.

公开(公告)号：US11822976B2

公开(公告)日：2023-11-21

申请号：US17538130

申请日：2021-11-30

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Harshit Daga ,  Ramana Rao V. R. Kompella

IPC: G06F9/44 ,  G06F9/54 ,  G06N20/00

CPC classification number: G06F9/541 ,  G06N20/00

Abstract: In one embodiment, a device presents information regarding an upstream machine learning workload and a downstream machine learning workload via a user interface. The device receives, via the user interface, a request to form a combined machine learning workload by connecting the upstream machine learning workload and the downstream machine learning workload. The device identifies, after receiving the request, a node associated with the upstream machine learning workload and a node associated with the downstream machine learning workload. The device forms the combined machine learning workload by configuring the node associated with the upstream machine learning workload to use one or more connector application programming interfaces to send data from the upstream machine learning workload to the node associated with the downstream machine learning workload for consumption.

公开(公告)号：US20230132213A1

公开(公告)日：2023-04-27

申请号：US17508241

申请日：2021-10-22

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Ali Payani ,  Ramana Rao V.R. Kompella

IPC: G06N20/20

Abstract: In one embodiment, a device receives, from a plurality of training nodes that train a set of machine learning models using local training datasets, bias metrics associated with those machine learning models for each feature of the local training datasets. The device generates aggregated machine learning models over time that aggregate the machine learning models trained by the plurality of training nodes. The device constructs, based on the bias metrics, bias lineages for the aggregated machine learning models. The device provides, based on the bias lineages, a bias lineage for a particular one of the aggregated machine learning models for display.

公开(公告)号：US20210279615A1

公开(公告)日：2021-09-09

申请号：US17173380

申请日：2021-02-11

Applicant: Cisco Technology, Inc.

Inventor： Hugo Latapie ,  Ozkan Kilic ,  Ramana Rao V. R. Kompella ,  Myungjin Lee ,  Simon Matthew Young

IPC: G06N5/04 ,  G06N5/02 ,  G06N3/04

Abstract: In one embodiment, a device maintains a metamodel that describes a monitored system. The metamodel comprises a plurality of layers ranging from a sub-symbolic space to a symbolic space. The device tracks updates to the metamodel over time. The device updates the metamodel based in part on sub-symbolic time series data generated by the monitored system. The device receives, from a learning agent, a request for the updates to a particular layer of the metamodel associated with a specified time period. The device provides, to the learning agent, data indicative of one or more updates to the particular layer of the metamodel associated with the specified time period.

公开(公告)号：US20250036961A1

公开(公告)日：2025-01-30

申请号：US18227535

申请日：2023-07-28

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Ganghua WANG ,  Ali PAYANI ,  Ramana Rao V. R. KOMPELLA

IPC: G06N3/098 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776

Abstract: In one embodiment, a supervisory device in a federated learning system generates an aggregated model that aggregates a plurality of machine learning models trained by trainer nodes in a federated learning system during a training round. The supervisory device computes an accuracy loss metric for the aggregated model. The supervisory device also computes a fairness loss metric for the aggregated model based on fairness-related metrics associated with the plurality of machine learning models trained by the trainer nodes. The supervisory device initiates an additional training round during which the trainer nodes retrain their machine learning models for aggregation by the apparatus, in accordance with a constrained optimization problem that seeks to optimize a tradeoff between accuracy and fairness associated with the aggregated model.

公开(公告)号：US20240378455A1

公开(公告)日：2024-11-14

申请号：US18196062

申请日：2023-05-11

Applicant: Cisco Technology, Inc.

Inventor： Jayanth Srinivasa ,  Myungjin Lee ,  Ramana Rao V. R. Kompella

IPC: G06N3/098

Abstract: In one embodiment, a device makes a determination that performance of a global model generated by aggregating local models trained by a plurality of trainer nodes in a federated learning system has experienced a degradation. The device selects, in response to the determination, a particular trainer node from among the plurality of trainer nodes to generate debugging metrics. The device provides an indication that the particular trainer node is a root cause of the degradation.

公开(公告)号：US20240256890A1

公开(公告)日：2024-08-01

申请号：US18101620

申请日：2023-01-26

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Dhruv Garg ,  Gaoxiang Luo ,  Ramana Rao V.R. Kompella

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: In one embodiment, a controller obtains state information from a plurality of nodes in a federated learning system. The controller determines, based on the state information, an adjustment to a topology of the federated learning system. The controller selects one or more nodes from among the plurality of nodes affected by the adjustment. The controller sends instructions to the one or more nodes, to implement the adjustment to the topology of the federated learning system.

公开(公告)号：US20230409983A1

公开(公告)日：2023-12-21

申请号：US17843264

申请日：2022-06-17

Applicant: Cisco Technology, Inc.

Inventor： Srinivas Siva Kumar Aradhyula ,  Eugenia Kim ,  Myungjin Lee ,  Ali Payani

IPC: G06N20/20 ,  H04L67/10 ,  H04L67/52

CPC classification number: G06N20/20 ,  H04L67/10 ,  H04L67/52

Abstract: In one embodiment, a controller for a federated learning system identifies a first dataset and a second dataset available to a particular node of the federated learning system. The first dataset comprises features that are common to all nodes of the federated learning system. The second dataset comprises features that are common only to a subset of nodes of the federated learning system. The controller configures the particular node to train a first model using the first dataset. The controller causes formation of a global model in the federated learning system that aggregates the first model from the particular node with models from all other nodes of the federated learning system. The controller configures the particular node to train a second model using the second dataset.

公开(公告)号：US20230168950A1

公开(公告)日：2023-06-01

申请号：US17538130

申请日：2021-11-30

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Harshit Daga ,  Ramana Rao V. R. Kompella

IPC: G06F9/54 ,  G06N20/00

CPC classification number: G06F9/541 ,  G06N20/00

Abstract: In one embodiment, a device presents information regarding an upstream machine learning workload and a downstream machine learning workload via a user interface. The device receives, via the user interface, a request to form a combined machine learning workload by connecting the upstream machine learning workload and the downstream machine learning workload. The device identifies, after receiving the request, a node associated with the upstream machine learning workload and a node associated with the downstream machine learning workload. The device forms the combined machine learning workload by configuring the node associated with the upstream machine learning workload to use one or more connector application programming interfaces to send data from the upstream machine learning workload to the node associated with the downstream machine learning workload for consumption.

公开(公告)号：US20250095348A1

公开(公告)日：2025-03-20

申请号：US18368790

申请日：2023-09-15

Applicant: Cisco Technology, Inc.

Inventor： Myungjin Lee ,  Gustav Adrian Baumgart ,  Jaemin Shin ,  Ramana Rao V.R. Kompella

IPC: G06V10/82 ,  G06V10/771

Abstract: In one implementation, a device generates outputs of nodes in a upstream layer of a partitioned neural network. The device assigns priorities to each of the outputs of the nodes. The device selects, based on the priorities, a subset of the outputs to send to a remote device. The device sends, via a computer network, the subset of the outputs to the remote device for input to a downstream layer of the partitioned neural network.