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.