在面向多应用的Society 5.0中实现FLaaS的高效客户端选择

中文摘要 AI 辅助

物联网（IoT）的快速发展导致传感器产生海量数据，促使需要先进的学习模型来分析这些数据以提供个性化服务。联邦学习（FL）作为一种解决方案应运而生，它通过在本地设备上构建模型并仅共享聚合洞察，提供保护用户隐私的分布式学习。本文探讨了物联网中的FL即服务（FLaaS），强调了其在跨应用协作学习方面的潜力，同时解决了安全性、隐私性以及优化分层架构以实现高效模型收敛和准确性等挑战。为了增强FL在物联网环境中的有效性，本研究通过评估延迟、能耗和链路状态等参数，专注于选择最优节点进行模型处理。所提出的方法旨在识别适合模型训练的客户端节点。性能评估在模拟物联网网络条件下使用人类活动识别数据集进行。将所提出的方法与基于随机和基于Q学习的客户端选择策略进行比较。实验结果显示在延迟和能效方面有所改善，同时保持了通信性能。研究结果强调了高效客户端选择机制在动态物联网生态系统中增强FLaaS以及支持Society 5.0中未来智能服务的潜力。

英文摘要

The rapid development of the Internet of Things (IoT) has led to the generation of vast amounts of data from sensors, prompting the need for advanced learning models to analyze this data for personalized services. Federated learning (FL) emerges as a solution, offering decentralized learning that preserves user privacy by building models on local devices and sharing only aggregated insights. This paper explores FL-as-a-service (FLaaS) in IoT, highlighting its potential for collaborative learning across applications while addressing challenges like security, privacy, and optimizing hierarchical architectures for efficient model convergence and accuracy. To enhance the effectiveness of FL in IoT environments, this study focuses on selecting optimal nodes for model processing through the evaluation of parameters such as delay, energy consumption, and link status. The proposed method aims to identify suitable client nodes for model training. Performance evaluation is conducted using a Human Activity Recognition dataset under simulated IoT network conditions. The proposed approach is compared against randomized and Q-learning based client selection strategies. Experimental results shows improvements in delay and energy efficiency while maintaining communication performance. The findings highlight the potential of efficient client selection mechanisms for enhancing FLaaS in dynamic IoT ecosystems and supporting future intelligent services in Society 5.0.

发表机构

• iitp.ac.in（印度理工学院帕拉普尔分校）

机构由 AI 辅助整理，请以论文原文为准。