公开(公告)号：US20240283712A1

公开(公告)日：2024-08-22

申请号：US18522999

申请日：2023-11-29

Applicant: Nokia Solutions and Networks Oy

Inventor： Borislava Gajic ,  Tejas Subramanya ,  Gerald Lehmann ,  Sivaramakrishnan Swaminathan

IPC: H04L41/50 ,  G06N20/00 ,  H04L41/16

CPC classification number: H04L41/50 ,  G06N20/00 ,  H04L41/16

Abstract: An apparatus for use by a communication network element or communication network function acting as an artificial intelligence, AI, machine learning, ML, management service consumer, the apparatus comprising at least one processing circuitry, and at least one memory for storing instructions that, when executed by the at least one processor, cause the apparatus at least to request an AI/ML energy consumption related parameter from an AI/ML management service producer offering services related to at least one AI/ML entity, to receive, from the AI/ML management service producer, the requested AI/ML energy consumption related parameter, and to process the AI/ML energy consumption related parameter for deriving an energy saving strategy considering the AI/ML energy consumption related parameter.

公开(公告)号：US20240249203A1

公开(公告)日：2024-07-25

申请号：US18408775

申请日：2024-01-10

Applicant: Nokia Solutions and Networks Oy

Inventor： Alberto Conte ,  Dario Bega ,  Tejas Subramanya ,  Abdelrahman Abdelkader

IPC: G06N20/20 ,  G06N3/0455 ,  H04L9/00

CPC classification number: G06N20/20 ,  G06N3/0455 ,  H04L9/008

Abstract: An apparatus for federated training, the apparatus comprising means for:



Transmitting a first implementation (22) of a data-processing model to a first distributed trainer, wherein the first implementation of the data-processing model comprises a first hidden part (221) and a first open part (222),
Transmitting a second implementation (23) of the data-processing model to a second distributed trainer, wherein the second implementation of the data-processing model comprises a second hidden part (231) and a second open part (232),
Receiving a first training gradient from the first distributed trainer and a second training gradient from the second distributed trainer, wherein the first gradient relates to the first open part of the first implementation of the data-processing model, wherein the second gradient relates to the second open part of the second implementation of the data-processing model,
Updating the data-processing model using the first gradient and the second gradient.

公开(公告)号：US20240152812A1

公开(公告)日：2024-05-09

申请号：US18470121

申请日：2023-09-19

Applicant: Nokia Solutions and Networks Oy

Inventor： Dario BEGA ,  Alberto Conte ,  Tejas Subramanya

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Disclosed are various example embodiments which may be configured to: receive, from a distributed node, local dataset information comprising characteristics of a local dataset of the distributed node, assign a score to the distributed node and/or determine whether the distributed node is a potential malicious distributed node based on the local dataset information, determine whether to select the distributed node for training a local model for managing a network in a federated learning mechanism based on the score assigned to the distributed node and/or whether the distributed node is a potential malicious distributed node, and send, to the distributed node, an indication as to whether the distributed node has been selected for training a model for managing a network in a federated learning mechanism.

公开(公告)号：US20240242119A1

公开(公告)日：2024-07-18

申请号：US18404327

申请日：2024-01-04

Applicant: Nokia Solutions and Networks Oy

Inventor： Chaitanya Aggarwal ,  Tejas Subramanya ,  Mehrnoosh Monshizadeh ,  Vikramajeet Khatri ,  Sina Hojjatinia

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: It may be that data collected, and retrieved, for a model training is not sufficient, and hence additional data may be needed. When it is determined that additional data is needed to complement the data retrieved, synthetic data is generated, and training data is obtained by combining the synthetic data with the data retrieved, and the model training is performed. A ratio of the synthetic data to the training data is determined and the ratio is indicated in a response to a request that caused the model to be trained.

公开(公告)号：US20250048418A1

公开(公告)日：2025-02-06

申请号：US18785195

申请日：2024-07-26

Applicant: Nokia Solutions and Networks Oy

Inventor： Tejas Subramanya ,  Borislava Gajic ,  Lalita Jagadeesan ,  Shushu Liu ,  Mehrnoosh Monshizadeh ,  Ilaria Malanchini

IPC: H04W72/566

Abstract: A first apparatus and a second apparatus (128) that are configured to manage or orchestrate network resources, or network services, or a machine learning model, or a composition of a machine learning model for a first stakeholder and a second stakeholder respectively, and first method comprising receiving, at the first apparatus (120), a requirement regarding a network intent, or for a network resource, or a network service, or a machine learning model, or a composition of a machine learning model, determining, at the first apparatus (120), a message regarding the requirement, sending the message to the second apparatus (128), and a second method comprising receiving, from the first apparatus (120) a first message regarding a requirement, and sending a second message regarding the requirement, to the first apparatus (120) or a first message regarding a network intent, and sending a second message regarding the network intent to the first apparatus (120).

公开(公告)号：US20240422594A1

公开(公告)日：2024-12-19

申请号：US18633229

申请日：2024-04-11

Applicant: Nokia Solutions and Networks Oy

Inventor： Tejas Subramanya ,  Sivaramakrishnan Swaminathan

IPC: H04W24/08 ,  H04L41/16 ,  H04L43/08

Abstract: Described herein is a first network element configured for supporting collection and/or evaluation of artificial intelligence/machine learning (AI/ML)-related operational statistics in a communications network, the first network element comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first network element at least to: determine one or more AI/ML-related operational statistics associated with the first network element; and report the determined one or more AI/ML-related operational statistics to a second network element of the communications network.

公开(公告)号：US20240242081A1

公开(公告)日：2024-07-18

申请号：US18406374

申请日：2024-01-08

Applicant: Nokia Solutions and Networks Oy

Inventor： Mehrnoosh MONSHIZADEH ,  Vikramajeet Khatri ,  Sina Hojjatinia ,  Chaitanya Aggarwal ,  Tejas Subramanya

IPC: G06N3/088 ,  G06N3/045

CPC classification number: G06N3/088 ,  G06N3/045

Abstract: Two competing machine learning based models, a first model for generating synthetic data, and a second model for classifying input data to synthetic data and real data, are trained by training the second model until its accuracy meets a preset rule, and then the first model is trained. After training the first model, training of models is repeated until an end criterium is met.