Lucca Gamballi, Daniel G. Tiglea, R. Candido, Magno T. M. Silva
{"title":"用于几何图形分类的MLP网络分布式训练","authors":"Lucca Gamballi, Daniel G. Tiglea, R. Candido, Magno T. M. Silva","doi":"10.14209/sbrt.2022.1570817366","DOIUrl":null,"url":null,"abstract":"— Multilayer perceptron neural networks are used to classify geometric figures using a distributed approach. Training data are divided among neural networks that communicate through a certain topology, in which each network does not have access to the training data of the others. Simulation results indicate that the performance achieved with distributed training and a suitable topology is similar to that observed with classical training. Thus, data privacy is guaranteed without loss of performance.","PeriodicalId":278050,"journal":{"name":"Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Treinamento distribuído de redes MLP para classificação de figuras geométricas\",\"authors\":\"Lucca Gamballi, Daniel G. Tiglea, R. Candido, Magno T. M. Silva\",\"doi\":\"10.14209/sbrt.2022.1570817366\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"— Multilayer perceptron neural networks are used to classify geometric figures using a distributed approach. Training data are divided among neural networks that communicate through a certain topology, in which each network does not have access to the training data of the others. Simulation results indicate that the performance achieved with distributed training and a suitable topology is similar to that observed with classical training. Thus, data privacy is guaranteed without loss of performance.\",\"PeriodicalId\":278050,\"journal\":{\"name\":\"Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14209/sbrt.2022.1570817366\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14209/sbrt.2022.1570817366","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Treinamento distribuído de redes MLP para classificação de figuras geométricas
— Multilayer perceptron neural networks are used to classify geometric figures using a distributed approach. Training data are divided among neural networks that communicate through a certain topology, in which each network does not have access to the training data of the others. Simulation results indicate that the performance achieved with distributed training and a suitable topology is similar to that observed with classical training. Thus, data privacy is guaranteed without loss of performance.