通信学报2023，Vol.44Issue(11)：183-200,18.DOI:10.11959/j.issn.1000-436x.2023191
异构联邦双向知识蒸馏传递半监督调制类型识别
Heterogeneous federated bidirectional knowledge distillation transfer semi-supervised modulation recognition
摘要
Abstract
The large-scale deployment and rapid development of the new generation mobile communication system un-derpin the widespread application of a massive and diverse range of Internet of things(IoT)devices.However,the dis-tributed application of IoT devices results to significant disparities in private data and substantial heterogeneity in local processing models,which severely limits the aggregation capability of global intelligent model.Therefore,to tackle the challenges of data heterogeneity,model heterogeneity,and insufficient labeling faced by intelligent modulation recogni-tion in cognitive IoT,an algorithm was proposed for heterogeneous federated bidirectional semi-supervised modulation recognition,which incorporated bidirectional knowledge distillation.In the proposed algorithm,a public pseudo dataset was generated by variational autoencoder in the cloud for supporting uplink global knowledge distillation,and adaptively sharing to the local devices for downlink heterogeneous knowledge distillation,while integrating a semi-supervised algo-rithm within the distillation process.The simulation results indicate that the proposed algorithm outperforms current fed-erated learning algorithms in terms of effectiveness and applicability in the field of communication signal processing.关键词
联邦半监督学习/模型异构/变分自编码器/知识蒸馏Key words
federated semi-supervised learning/model heterogeneity/variational autoencoder/knowledge distillation分类
信息技术与安全科学引用本文复制引用
齐佩汉,丁渊磊,尹凯,徐佳波,李赞..异构联邦双向知识蒸馏传递半监督调制类型识别[J].通信学报,2023,44(11):183-200,18.基金项目
国家自然科学基金资助项目(No.62171334,No.61971337,No.61825104) Foundation Item:The National Natural Science Foundation of China(No.62171334,No.61971337,No.61825104) （No.62171334,No.61971337,No.61825104）
