计算机工程与应用2025，Vol.61Issue(19)：92-105,14.DOI:10.3778/j.issn.1002-8331.2412-0444
面向分层联邦学习框架的自适应异步聚合算法
Adaptive Asynchronous Aggregation Algorithm for Hierarchical Federated Learning Framework
摘要
Abstract
Federated learning,as a distributed training technology,has been widely applied in the training of large-scale neural network models.However,due to issues such as high communication costs among Internet of things devices,hetero-geneous data distribution,and privacy and security,it poses significant challenges to federated learning training.To address the above problems,an adaptive asynchronous aggregation algorithm based on hierarchical federated learning(HASFL)is proposed.Firstly,the client processes the local model using a sparse matrix and a local differential privacy policy and uploads it to the edge server.When the edge server performs edge aggregation,it eliminates malicious client devices through an anomaly detection mechanism.Secondly,at the edge end,the influence of model aging differences during the update process on the global model is reduced through an adaptive asynchronous aggregation algorithm based on dynamic grouping and data volume weighting.Finally,on the server side,an adaptive dynamic aggregation algorithm based on the scoring mechanism is used to evaluate the contributions of edge servers and dynamically adjust their weights to improve the convergence speed of the model and the stability of the system.The experimental results show that,compared with the FedAsync algorithm,HASFL shortens the time to achieve the target accuracy by approximately 40%,and is superior to other comparison algorithms in terms of convergence speed and stability.关键词
分层联邦学习/差分隐私/动态分组/异步聚合/异常检测/自适应聚合Key words
hierarchical federated learning/differential privacy/dynamic grouping/asynchronous aggregation/anomaly detection/adaptive aggregation分类
信息技术与安全科学引用本文复制引用
徐飞,申奥祥,赵毅恒,邓岩,宁辛,王星..面向分层联邦学习框架的自适应异步聚合算法[J].计算机工程与应用,2025,61(19):92-105,14.基金项目
陕西省科技厅区域创新能力指导计划(20122qfy01-14) （20122qfy01-14）
陕西省教育厅服务地方专项计划项目(23JC039) （23JC039）
咸阳市科技计划重点研发项目(2021ZDYF-NY-0019). （2021ZDYF-NY-0019）
