Path:
Home >
GB/T >
Page236 > GB/T 46284-2025
Home >
Standard_List >
GB/T >
Page236 > GB/T 46284-2025
US$519.00 ยท In stock
Delivery: <= 5 days. True-PDF full-copy in English will be manually translated and delivered via email.
GB/T 46284-2025: Artificial intelligence - Technical specifications of federated learning
Status: Valid
| Standard ID | Contents [version] | USD | STEP2 | [PDF] delivered in | Standard Title (Description) | Status | PDF |
| GB/T 46284-2025 | English | 519 |
Add to Cart
|
5 days [Need to translate]
|
Artificial intelligence - Technical specifications of federated learning
| Valid |
GB/T 46284-2025
|
PDF similar to GB/T 46284-2025
Basic data
| Standard ID | GB/T 46284-2025 (GB/T46284-2025) |
| Description (Translated English) | Artificial intelligence - Technical specifications of federated learning |
| Sector / Industry | National Standard (Recommended) |
| Classification of Chinese Standard | L60 |
| Classification of International Standard | 35.240 |
| Word Count Estimation | 26,245 |
| Date of Issue | 2025-10-05 |
| Date of Implementation | 2025-10-05 |
| Issuing agency(ies) | State Administration for Market Regulation and Standardization Administration of China |
GB/T 46284-2025: Artificial intelligence - Technical specifications of federated learning
---This is an excerpt. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.), auto-downloaded/delivered in 9 seconds, can be purchased online: https://www.ChineseStandard.net/PDF.aspx/GBT46284-2025
ICS 35.240
CCSL60
National Standards of the People's Republic of China
Artificial Intelligence Federated Learning Technical Specifications
Published on 2025-10-05
Implemented on October 5, 2025
State Administration for Market Regulation
The State Administration for Standardization issued a statement.
Table of Contents
Preface III
1.Scope 1
2 Normative References 1
3.Terms and Definitions 1
4.Abbreviations 2
5.Overview of Federated Learning Systems 2
5.1 Federated Learning System Architecture 2
5.2 Participants in Federated Learning 3
5.3 Federated Learning Task Process 5
6.Functional Requirements of the Federated Learning System 7.
6.1 Basic Technology Module 7
6.2 Calculation Module 8
6.3 Management Module 8
7.Federated Learning System Performance Requirements 10
7.1 Federal Data Probing 10
7.2 Federated Modeling 10
7.3 Federalized Reasoning 11
7.4 Federal Data Quality and Incentive Mechanisms 11
8.Functional Testing Methods for Federated Learning Systems 11
8.1 Basic Technology Module 11
8.2 Calculation Module 12
8.3 Management Module 13
9.Performance Testing Methods for Federated Learning Systems 15
9.1 Federal Data Probing 15
9.2 Federated Modeling 16
9.3 Federalized Reasoning 16
9.4 Federal Data Quality and Incentive Mechanisms 16
Reference 18
Foreword
This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part 1.Structure and Drafting Rules of Standardization Documents".
Drafting.
Please note that some content in this document may involve patents. The issuing organization of this document assumes no responsibility for identifying patents.
This document was proposed and is under the jurisdiction of the National Information Technology Standardization Technical Committee (SAC/TC28).
This document was drafted by. China Electronics Technology Standardization Institute, Shenzhen Qianhai WeBank Co., Ltd., and China Mobile Communications Corporation.
Research Institute of China Electronics Technology Group Corporation Limited, China Mobile Communications Group Co., Ltd., Industrial and Commercial Bank of China Limited, China Electronics Technology Big Data Research Institute Co., Ltd.
Company, Shenzhen Insight Wisdom Technology Co., Ltd., Inspur Software Technology Co., Ltd., Inspur Software Group Co., Ltd., Shandong University, Harbin
Shenzhen Institute of Technology, Inspur Electronic Information Industry Co., Ltd., China Mobile (Suzhou) Software Technology Co., Ltd., AsiaInfo Technologies (China) Co., Ltd.
Limited Company, Beijing Shudu Technology Co., Ltd., Institute of Computing Technology, Chinese Academy of Sciences, OPPO Guangdong Mobile Communications Co., Ltd., Shanghai
Artificial Intelligence Industry Association, Shanghai Computer Software Technology Development Center, Institute of Automation, Chinese Academy of Sciences, China Telecom Research
Institute, China Southern Power Grid Data Platform and Security (Guangdong) Co., Ltd., Shanghai Lingshu Zhonghe Information Technology Co., Ltd., and Mashang Consumer Finance Co., Ltd.
Limited Liability Company, Bank of Communications Co., Ltd., Hangzhou High-tech Zone (Binjiang) Blockchain and Data Security Research Institute, Hangzhou QuChain Technology Co., Ltd.
Beijing Haohan Depth Information Technology Co., Ltd.
This document was drafted by. Fan Kefeng, Fan Lixin, Ma Shanshan, Qian Wenjun, Jia Yunfei, Xia Zhiyuan, Guan Guilin, Wang Wanwan, Lin Yiwei, and Meng Jian.
Wu Jianlong, Nie Liqiang, Di Helang, Wang Xiaobin, Jing Qin, Jin Yinyu, Wang Yuwei, Fu Yanyan, Rao Xue, Chen Mingang, He Saike, Wu Zuping, Yang Guang
Lan Chunjia, Deng Weihong, Wang Guangzhong, Du Jingyi, Zhang Qing, Liu Shaokai.
Artificial Intelligence Federated Learning Technical Specifications
1.Scope
This document establishes the architecture, participants, and workflow of the federated learning system, and specifies its functional requirements and performance.
The requirements describe the corresponding testing methods.
This document applies to the design, development, testing, use, and maintenance of federated learning systems, and to conducting horizontal comparisons of federated learning application systems.
Selection.
Note. This document only covers the system functionalities and performance requirements for implementing and supporting federated learning methods; it does not provide details on general-purpose computer hardware and software or communication equipment itself.
Detailed regulations.
2 Normative references
The contents of the following documents, through normative references within the text, constitute essential provisions of this document. Dated citations are not included.
For references to documents, only the version corresponding to that date applies to this document; for undated references, the latest version (including all amendments) applies.
This document.
GB/T 41867 Terminology for Information Technology and Artificial Intelligence
3.Terms and Definitions
The terms and definitions defined in GB/T 41867 and the following terms and definitions apply to this document.
3.1
Federated learning
Multiple participants interact in a manner that protects privacy, while ensuring that their original data does not leave the trusted domain defined by the data provider.
Methods for collaboratively completing artificial intelligence and machine learning tasks by using inter-data.
3.2
A comprehensive computer and communication system for implementing federated learning methods.
Note. This system includes not only various computer hardware and software (such as algorithms, CPUs, GPUs, etc.) for executing federated learning algorithms, but also supports remote communication between nodes.
The communication protocols and equipment used for data transmission (e.g., network devices and data transmission protocols). This system ensures that each participant can protect the original data locally.
Under the premise of privacy, machine learning tasks can be completed collaboratively by exchanging intermediate model updates (such as gradients or parameters).
3.3
In federated learning methods that divide data into samples, each participant's dataset has the same feature space but different sample spaces.
3.4
In a feature-based federated learning approach, each participant's dataset has the same sample space but different feature spaces.