面向算力物联网的联邦学习系统及设计研究进展

鲁剑锋; 祁盼; 潘林雨; 李冰; 曹书琴; 靳延安

doi:10.11959/j.issn.2096-3750.2024.00438

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面向算力物联网的联邦学习系统及设计研究进展

专题：算力物联网 | 更新时间：2025-01-20

- 面向算力物联网的联邦学习系统及设计研究进展
- Recent advances on federated learning systems and the design for computing power Internet of things
- 物联网学报 2024年8卷第4期页码：70-88
- 作者机构：
  
  1.武汉科技大学计算机科学与技术学院，湖北武汉 430081
  2.中国人民解放军91999部队，山东青岛 266001
  3.湖北经济学院信息管理学院，湖北武汉 430205
- 作者简介：
  
  [ "鲁剑锋（1982‒ ），男，博士，武汉科技大学计算机科学与技术学院教授、博士生导师，主要研究方向为边缘智能、联邦学习和群智计算。" ]
  [ "祁盼（2001‒ ），男，武汉科技大学计算机科学与技术学院硕士生，主要研究方向为联邦学习资源优化和激励机制。" ]
  [ "潘林雨（1980‒ ），男，中国人民解放军91999部队海军专业技术上校，主要研究方向为作战数据应用和智能计算。" ]
  [ "李冰（1995‒ ），女，武汉科技大学计算机科学与技术学院博士生，主要研究方向为联邦学习和群智计算。" ]
  [ "曹书琴（1992‒ ），女，博士，武汉科技大学计算机科学与技术学院讲师，主要研究方向为联邦学习、车联网和交通数据挖掘。" ]
  [ "靳延安（1975‒ ），男，博士，湖北经济学院信息管理学院副教授，主要研究方向为信息智能处理、数据挖掘和智慧养老。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62072411;62372343;62402352);浙江省自然科学基金资助项目(LR21F020001);湖北省重点研发项目(2023BEB024);武汉东湖新技术开发区“揭榜挂帅”项目(2024KJB301)
- DOI：10.11959/j.issn.2096-3750.2024.00438
  中图分类号：
- 纸质出版日期：2024-12-10，
  
  收稿日期：2024-10-08，
  
  修回日期：2024-11-19，
- 稿件说明：
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鲁剑锋, 祁盼, 潘林雨, 等. 面向算力物联网的联邦学习系统及设计研究进展[J]. 物联网学报, 2024,8(4):70-88.

LU JIANFENG, QI PAN, PAN LINYU, et al. Recent advances on federated learning systems and the design for computing power Internet of things. [J]. Chinese journal on internet of things, 2024, 8(4): 70-88.
鲁剑锋, 祁盼, 潘林雨, 等. 面向算力物联网的联邦学习系统及设计研究进展[J]. 物联网学报, 2024,8(4):70-88. DOI： 10.11959/j.issn.2096-3750.2024.00438.

LU JIANFENG, QI PAN, PAN LINYU, et al. Recent advances on federated learning systems and the design for computing power Internet of things. [J]. Chinese journal on internet of things, 2024, 8(4): 70-88. DOI： 10.11959/j.issn.2096-3750.2024.00438.

摘要

算力物联网（CPIoT

computing power Internet of things）通过整合物联网（IoT

Internet of things）设备与强大的计算资源，为数据密集型任务提供了强大的支持，实现了智能决策。在CPIoT的隐私保护需求背景下，联邦学习（FL

federated learning）作为一种旨在保护数据隐私、进行分布式学习的技术，为解决数据“孤岛”问题、执行复杂训练任务及大模型训练提供了新途径。虽然研发人员一直致力于开发更加成熟的FL系统以适应CPIoT环境，但目前的研究在深入探讨FL系统设计技术的优势与短板、技术特点与差异、支持与适用情况等方面仍然存在不足。因此，首先深入研究了当前业内有影响力的FL系统，包括开源框架和基准测试平台，并在CPIoT的不同技术维度上深入对比分析系统设计差异，建立了CPIoT环境下详细的FL开源框架与基准测试平台的选择标准及建议，使开发人员可以更加高效地选择合适的框架及平台。然后，列举了多种CPIoT场景下FL系统的选择与完整系统搭建的实验，使开发人员可以更好地借助上述技术实现FL应用。最后，总结了FL系统设计领域的标准化现状和发展挑战，并对未来发展进行了展望。旨在全面概述FL系统及其设计研究进展，为推动CPIoT与FL网络的深度融合提供参考，也为未来研究提供思路。

Abstract

Computing power Internet of things (CPIoT) integrates Internet of things (IoT) devices with substantial computational resources to support data-intensive tasks

facilitating intelligent decision-making. Within the context of privacy protection requirements for CPIoT

federated learning (FL) that is a distributed learning technique upholds data privacy

and offers a novel approach to addressing data silos for executing complex training tasks

and training large models. Although researchers have been committed to develop more mature federated learning systems to adapt to the CPIoT environment

current research lacks in-depth exploration of the strengths and limitations

technical features and differences

and support and applicability of federated learning system design techniques. Firstly

the most influential federated learning systems in the industry were studied

including open-source frameworks and benchmarking platforms. The system design differences in various technical dimensions of CPIoT in an in-depth comparison were analyzed. Detailed criteria and recommendations for selecting open-source frameworks and benchmarking platforms in the CPIoT environment were established

so that developers could efficiently choose the most suitable frameworks and platforms. Seeondly

various experiments for selecting federated learning systems and building complete systems were presented in multiple CPIoT scenarios

to assist developers in better realizing federated learning applications by utilizing the aforementioned technologies. Finally

the current state of standardization and development challenges in the field of federated learning system design were summarized

and future development prospects were discussed. The purpose is to provide a comprehensive overview of FL systems and the design research progress

serving as a reference for the deep integration of CPIoT and FL networks and offering insights for future research.

关键词

算力物联网联邦学习开源框架基准测试平台计算范例

Keywords

CPIoTFLopen-source frameworkbenchmarking platformcomputing paradigm

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