相关实验视频

Updated: Jan 12, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

联合异步图表注意网络,具有结构语义嵌入,用于多标签图表分类

Xinwu Ji¹, Yijing Zhang¹, Kaihong Zheng²

¹China Southern Power Grid, Yunnan Power Grid Co., Ltd, Kunming, 650000, China.

Scientific reports

|November 4, 2025

概括

与图形神经网络 (FL-GNN) 联合学习现在可以更好地处理标签语义和图形异质性. 新的FasSGAT模型通过整合标签嵌入和结构敏感聚合来改善多标签分类.

科学领域:

人工智能的人工智能
机器学习机器学习
网络科学网络科学

背景情况:

联合学习 (FL) 允许图形神经网络 (GNN) 的隐私保护培训.
传统的FL-GNN经常忽视标签语义,并与客户端数据异质性作斗争.
客户端图表表示不一致性和分布式图表变化阻碍了FL-GNN的性能.

研究的目的:

引入一个新的FL-GNN框架FasSGAT,该框架针对标签语义和图形异质性进行多标签分类.
通过结合标签语义和减轻客户内部和客户间异质性来提高FL-GNN的性能.
开发一个结构敏感的异步聚合机制,用于强大的全球模型构建.

主要方法:

开发了客户端特定的标签语义嵌入模块,使用标签语义分布图.
将标签嵌入和结构敏感的光谱特征集成到多标签分类器中,以解决客户端异质性问题.
实现了一个新的服务器级结构敏感的异步聚合机制,利用图谱特征.

主要成果:

FasSGAT有效地从标签语义分布图中学习特征编码.
该模型成功地通过使用专门的光谱特征来缓解客户端异质性.
实验结果表明,FasSGAT在多标签基准上比传统的FL方法表现优越.

结论:

FasSGAT在GNN的联合学习方面取得了重大进展,特别是在多标签分类方面.
该框架成功地解决了标签语义和图形异质性的关键挑战.
拟议的方法提高了隐私敏感的分布式图形学习场景中的模型性能和稳定性.

关键词:

联合学习是联合学习.图表注意力神经网络的神经网络.多标签学习多标签学习

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

相关概念视频

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Multiple Bar Graph

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...