公开(公告)号：US20250086495A1

公开(公告)日：2025-03-13

申请号：US18367393

申请日：2023-09-12

Applicant: Adobe Inc.

Inventor： Saayan Mitra ,  Xiang Chen ,  Sapthotharan Krishnan Nair ,  Renzhi Wu ,  Anup Rao

IPC: G06N20/00

Abstract: An edge node included in a decentralized edge computing network generates a federated partial-data aggregation machine learning model. The edge node learns one or more model parameters via machine learning techniques and receives one or more auxiliary model parameters from additional edge nodes in the decentralized edge computing network, such as from a neighbor node group. In some cases, a neighbor node is identified in response to determining that the neighbor node includes a model with a relatively high estimated relevance to the model of the edge node. The edge node modifies the model to include an aggregation of the learned model parameters and the received auxiliary parameters. Respective weights are learned for the learned model parameters and also for the received auxiliary parameters. During training to learn the respective weights, the edge node stabilizes the learned model parameters and the received auxiliary parameters.