文献类型：期刊文献

英文题名：Optimization Algorithm for Cloud-Edge-Terminal Federated Learning in the IoT

作者：Xiao, Kaile[1]; Zhang, Qian[2]; Lang, Huan[2]

第一作者：Xiao, Kaile

机构：[1] School of Applied Science and Technology, Beijing Union University, Beijing, China; [2] Coordination Center of China, National Computer Network Emergency Response Technical Team, China

第一机构：北京联合大学应用科技学院

年份：2025

起止页码：320-323

外文期刊名：2025 4th International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2025

收录：EI(收录号：20254719522897)

语种：英文

外文关键词：Cloud computing architecture - Cloud security - Computer architecture - Data aggregation - Edge computing - Learning algorithms - Network security - Optimization

摘要：With the extensive application of the Internet of Things (IoT) in multiple fields, diversified scenarios put forward strict and differentiated requirements for quality of service. The traditional centralized cloud computing architecture is difficult to meet the needs of large-scale data processing. Although federated learning is a potential solution for model training in cloud-edgeend architectures, it faces challenges such as non-IID data and differences in device computing resources. Moreover, IoT systems have the coupling problem of security constraints and delay optimization. To this end, this paper designs an adaptive weight aggregation mechanism to dynamically sense data distribution and adjust gradient aggregation weights by combining the contribution of device computing resources, so as to improve the convergence speed and adaptability of the model in non-IID environments; ADMM is introduced to solve the coupling problem of security and delay through cross-layer collaborative solving. Comparative experiments on the MNIST dataset show that the algorithm performs better in terms of accuracy, precision, recall rate, and F1 score and other indicators. ? 2025 IEEE.