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.
Recent advances on federated learning systems and the design for computing power Internet of things
Computing power Internet of things(CPIoT)integrates Internet of things(IoT)devices with substantial compu-tational 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 envi-ronment,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 sys-tem design differences in various technical dimensions of CPIoT in an in-depth comparison were analyzed.Detailed crite-ria 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,vari-ous 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 compre-hensive 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.
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